{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "collectible-necklace",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "import codecs\n",
    "import math\n",
    "import os\n",
    "import random\n",
    "from collections import Counter\n",
    "from glob import glob\n",
    "from io import BytesIO\n",
    "import json\n",
    "import re\n",
    "\n",
    "\n",
    "from keras.utils import np_utils, Sequence\n",
    "from keras.layers import Dense, Dropout, BatchNormalization, GlobalAveragePooling2D\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Model\n",
    "from keras.callbacks import ReduceLROnPlateau, ModelCheckpoint, EarlyStopping\n",
    "from keras import regularizers\n",
    "from keras.optimizers import Adam\n",
    "from keras.applications.resnet50 import ResNet50\n",
    "from keras.applications.resnet50 import preprocess_input\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import requests as req\n",
    "\n",
    "from PIL import Image\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "understood-staff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<absl.flags._flagvalues.FlagHolder at 0x7fe7d76b7f98>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 设备控制台输出配置\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "config = tf.compat.v1.ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "config.gpu_options.per_process_gpu_memory_fraction = 0.7\n",
    "sess = tf.compat.v1.Session(config=config)\n",
    "\n",
    "# 全局配置文件\n",
    "tf.compat.v1.app.flags.DEFINE_string('data_dir', './data/base_data', '全部数据')\n",
    "tf.compat.v1.app.flags.DEFINE_string('test_data', './data/test', '测试集')\n",
    "tf.compat.v1.app.flags.DEFINE_string('train_data', './data/train', '训练集')\n",
    "tf.compat.v1.app.flags.DEFINE_string('validation_data', './data/validation', '验证集')\n",
    "tf.compat.v1.app.flags.DEFINE_integer('num_classes', 40, '垃圾分类数目')\n",
    "tf.compat.v1.app.flags.DEFINE_integer('input_size', 224, '模型输入图片大小')\n",
    "tf.compat.v1.app.flags.DEFINE_integer('batch_size', 16, '图片批处理大小')\n",
    "tf.compat.v1.app.flags.DEFINE_float('learning_rate', 1e-4, '学习率')\n",
    "tf.compat.v1.app.flags.DEFINE_integer('max_epochs', 4, '步长')\n",
    "tf.compat.v1.app.flags.DEFINE_string('train_local', './output_model/', '训练输出文件夹')\n",
    "tf.compat.v1.app.flags.DEFINE_integer('keep_weights_file_num', 20, '如果设置为-1，则文件保持的最大权重数表示无穷大')\n",
    "FLAGS = tf.compat.v1.app.flags.FLAGS\n",
    "\n",
    "#由于juyterlab的问题，加上这一句\n",
    "tf.compat.v1.app.flags.DEFINE_string('f', '', 'kernel')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "shared-adrian",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 垃圾分类数目\n",
    "num_classes = FLAGS.num_classes\n",
    "\n",
    "# 获取所有标签文件路径\n",
    "data_dir = FLAGS.data_dir\n",
    "label_files = glob(os.path.join(data_dir, '*.txt'))\n",
    "random.shuffle(label_files)\n",
    "\n",
    "input_size = FLAGS.input_size\n",
    "batch_size = FLAGS.batch_size\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "designing-auction",
   "metadata": {},
   "source": [
    "# 数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "wooden-valley",
   "metadata": {},
   "outputs": [],
   "source": [
    "class BaseSequence(Sequence):\n",
    "    \"\"\"\n",
    "    基础的数据流生成器，每次迭代返回一个batch\n",
    "    BaseSequence可直接用于fit_generator的generator参数\n",
    "    fit_generator会将BaseSequence再次封装为一个多进程的数据流生成器\n",
    "    而且能保证在多进程下的一个epoch中不会重复取相同的样本\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, img_paths, labels, batch_size, img_size, is_train):\n",
    "        assert len(img_paths) == len(labels), \"len(img_paths) must equal to len(lables)\"\n",
    "        assert img_size[0] == img_size[1], \"img_size[0] must equal to img_size[1]\"\n",
    "        ## (?,41)\n",
    "        self.x_y = np.hstack((np.array(img_paths).reshape(len(img_paths), 1), np.array(labels)))\n",
    "        self.batch_size = batch_size\n",
    "        self.img_size = img_size\n",
    "        self.is_train = is_train\n",
    "        if self.is_train:\n",
    "            # 通过实时数据增强生成张量图像数据批次。数据将不断循环（按批次）\n",
    "            # 初始化ImageDataGenerator\n",
    "            train_datagen = ImageDataGenerator(\n",
    "                rotation_range=30,  # 图片随机转动角度\n",
    "                width_shift_range=0.2,  # 浮点数，图片宽度的某个比例，数据提升时图片水平偏移的幅度\n",
    "                height_shift_range=0.2,  # 浮点数，图片高度的某个比例，数据提升时图片竖直偏移的幅度\n",
    "                shear_range=0.2,  # 剪切强度（逆时针方向的剪切变换角度）\n",
    "                zoom_range=0.2,  # 随机缩放的幅度，\n",
    "                horizontal_flip=True,  # 随机水平翻转\n",
    "                vertical_flip=True,  # 随机竖直翻转\n",
    "                fill_mode='nearest'\n",
    "            )\n",
    "            self.train_datagen = train_datagen\n",
    "\n",
    "    # batch的个数\n",
    "    def __len__(self):\n",
    "        return math.ceil(len(self.x_y) / self.batch_size)\n",
    "\n",
    "    @staticmethod\n",
    "    def center_img(img, size=None, fill_value=255):\n",
    "        \"\"\"\n",
    "        center img in a square background\n",
    "        \"\"\"\n",
    "        h, w = img.shape[:2]\n",
    "        if size is None:\n",
    "            size = max(h, w)\n",
    "        # h,w,channel\n",
    "        shape = (size, size) + img.shape[2:]\n",
    "        background = np.full(shape, fill_value, np.uint8)\n",
    "        center_x = (size - w) // 2\n",
    "        center_y = (size - h) // 2\n",
    "        background[center_y:center_y + h, center_x:center_x + w] = img\n",
    "        return background\n",
    "\n",
    "    # 处理图片标准化\n",
    "    def preprocess_img(self, img_path):\n",
    "        \"\"\"\n",
    "        image preprocessing\n",
    "        you can add your special preprocess method here\n",
    "        \"\"\"\n",
    "        img = Image.open(img_path)\n",
    "        img = img.resize((256, 256))\n",
    "        img = img.convert('RGB')\n",
    "        img = np.array(img)\n",
    "        img = img[16:16 + 224, 16:16 + 224]\n",
    "        return img\n",
    "\n",
    "    # 一种正则化方法，在训练时随机把图片的一部分减掉，这样能提高模型的鲁棒性\n",
    "    def cutout_img(self, img):\n",
    "        cut_out = Cutout(n_holes=1, length=40)\n",
    "        img = cut_out(img)\n",
    "        return img\n",
    "\n",
    "    # 第n个batch\n",
    "    def __getitem__(self, idx):\n",
    "\n",
    "        # 图片路径\n",
    "        batch_x = self.x_y[idx * self.batch_size: (idx + 1) * self.batch_size, 0]\n",
    "        # 图片标签\n",
    "        batch_y = self.x_y[idx * self.batch_size: (idx + 1) * self.batch_size, 1:]\n",
    "        \n",
    "        # 这里是像素数组 （224，224，3）\n",
    "        batch_x = np.array([self.preprocess_img(img_path) for img_path in batch_x])\n",
    "        # smooth labels\n",
    "        batch_y = np.array(batch_y).astype(np.float32) * (1 - 0.05) + 0.05 / 40\n",
    "\n",
    "        # # 训练集数据增强\n",
    "        if self.is_train:\n",
    "            # 从给定的一维数组生成随机样本\n",
    "            indexs = np.random.choice([0, 1, 2], batch_x.shape[0], replace=True, p=[0.4, 0.4, 0.2])\n",
    "            mask_indexs = np.where(indexs == 1)\n",
    "            multi_indexs = np.where(indexs == 2)\n",
    "\n",
    "            if len(multi_indexs):\n",
    "                # 数据增强\n",
    "                multipy_batch_x = batch_x[multi_indexs]\n",
    "                multipy_batch_y = batch_y[multi_indexs]\n",
    "\n",
    "                train_datagenerator = self.train_datagen.flow(multipy_batch_x, multipy_batch_y,\n",
    "                                                              batch_size=self.batch_size)\n",
    "                (multipy_batch_x, multipy_batch_y) = train_datagenerator.next()\n",
    "\n",
    "                batch_x[multi_indexs] = multipy_batch_x\n",
    "                batch_y[multi_indexs] = multipy_batch_y\n",
    "\n",
    "            if len(mask_indexs[0]):\n",
    "                # 随机遮挡\n",
    "                mask_batch_x = batch_x[mask_indexs]\n",
    "                mask_batch_y = batch_y[mask_indexs]\n",
    "                mask_batch_x = np.array([self.cutout_img(img) for img in mask_batch_x])\n",
    "\n",
    "                batch_x[mask_indexs] = mask_batch_x\n",
    "                batch_y[mask_indexs] = mask_batch_y\n",
    "\n",
    "        # 预处理\n",
    "        batch_x = np.array([preprocess_input(img) for img in batch_x])\n",
    "\n",
    "        return batch_x, batch_y\n",
    "\n",
    "    # 在迭代之间修改数据集\n",
    "    def on_epoch_end(self):\n",
    "        \"\"\"Method called at the end of every epoch.\n",
    "        \"\"\"\n",
    "        np.random.shuffle(self.x_y)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "broad-tourism",
   "metadata": {},
   "source": [
    "np.hstack是数组拼接函数，横向拼接，将图片路径和图片标签放入x_y中的一列\n",
    "```python\n",
    "self.x_y = np.hstack((np.array(img_paths).reshape(len(img_paths), 1), np.array(labels)))\n",
    "```\n",
    "# ImageDataGenerator\n",
    "ImageDataGenerator()是keras.preprocessing.image模块中的图片生成器，可以每一次给模型“喂”一个batch_size大小的样本数据，同时也可以在每一个批次中对这batch_size个样本数据进行增强，扩充数据集大小，增强模型的泛化能力。比如进行旋转，变形，归一化等等。\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "lucky-grenada",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def data_flow(train_data_dir, batch_size, num_classes, input_size):\n",
    "    img_paths = []\n",
    "    labels = []\n",
    "\n",
    "    for index, file_path in enumerate(label_files):\n",
    "        with codecs.open(file_path, 'r', 'utf-8') as f:\n",
    "            line = f.readline()\n",
    "        line_split = line.strip().split(', ')\n",
    "        if len(line_split) != 2:\n",
    "            print('%s contain error lable' % os.path.basename(file_path))\n",
    "            continue\n",
    "        img_name = line_split[0]\n",
    "        label = int(line_split[1])\n",
    "        img_paths.append(os.path.join(data_dir, img_name))\n",
    "        labels.append(label)\n",
    "\n",
    "    \n",
    "\n",
    "    # 画分训练集，验证集，测试集\n",
    "    train_img_paths, test_img_paths, train_labels, test_labels = \\\n",
    "            train_test_split(img_paths, labels, stratify=labels,test_size=0.10, random_state=0)\n",
    "    train_img_paths, validation_img_paths, train_labels, validation_labels = \\\n",
    "            train_test_split(train_img_paths, train_labels, stratify=train_labels, test_size=0.15, random_state=0)\n",
    "    print('total samples: %d, training samples: %d, validation samples: %d, test samples: %d' % (len(img_paths), len(train_img_paths), len(validation_img_paths), len(test_img_paths)))\n",
    "\n",
    "    # 将类向量（整数）转换为二进制类矩阵。\n",
    "    train_labels = np_utils.to_categorical(train_labels, num_classes)\n",
    "    validation_labels = np_utils.to_categorical(validation_labels, num_classes)\n",
    "    \n",
    "    \n",
    "    # 训练集随机增强图片\n",
    "    # Sequence是进行多进程处理的更安全的方法。这种结构保证网络在每个时期每个样本只训练一次\n",
    "    train_sequence = BaseSequence(train_img_paths, train_labels, batch_size, [input_size, input_size],is_train=True)\n",
    "    validation_sequence = BaseSequence(validation_img_paths, validation_labels, batch_size, [input_size, input_size],is_train=True)\n",
    "    return train_sequence, validation_sequence, test_img_paths, test_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "solved-artwork",
   "metadata": {},
   "source": [
    "# 数据构建"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "german-blackberry",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total samples: 14683, training samples: 11231, validation samples: 1983, test samples: 1469\n"
     ]
    }
   ],
   "source": [
    "# 数据构建\n",
    "train_sequence, validation_sequence, test_img_paths, test_labels = data_flow(FLAGS.data_dir, FLAGS.batch_size,\n",
    "                                                FLAGS.num_classes, FLAGS.input_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "constant-marijuana",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载测试数据\n",
    "def load_test_data(FLAGS, test_img_paths, test_labels):\n",
    "    test_data = []\n",
    "    img_names = []\n",
    "    test_labels = []\n",
    "    for index, img_path in enumerate(test_img_paths):\n",
    "        # 处理图片\n",
    "        img = preprocess_img(img_path,FLAGS.input_size)\n",
    "        test_data.append(preprocess_img(img_path,FLAGS.input_size))\n",
    "    # test_data = np.array(test_data)\n",
    "    return test_data,test_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fluid-audit",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['./data/base_data/img_3934.jpg' './data/base_data/img_16416.jpg'\n",
      " './data/base_data/img_11835.jpg' './data/base_data/img_7874.jpg'\n",
      " './data/base_data/img_6887.jpg' './data/base_data/img_8000.jpg'\n",
      " './data/base_data/img_3037.jpg' './data/base_data/img_9067.jpg'\n",
      " './data/base_data/img_15924.jpg' './data/base_data/img_16681.jpg'\n",
      " './data/base_data/img_11990.jpg' './data/base_data/img_3654.jpg'\n",
      " './data/base_data/img_9899.jpg' './data/base_data/img_4518.jpg'\n",
      " './data/base_data/img_11235.jpg' './data/base_data/img_5202.jpg']\n",
      "[['0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '1.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0']\n",
      " ['0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '1.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0']\n",
      " ['0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '1.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0']\n",
      " ['0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0' '1.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0']\n",
      " ['0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '1.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0']\n",
      " ['0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0' '1.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0']\n",
      " ['0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '1.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0']\n",
      " ['0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '1.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0']\n",
      " ['0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '1.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0']\n",
      " ['0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '1.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0']\n",
      " ['0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '1.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0']\n",
      " ['0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '1.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0']\n",
      " ['0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '1.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0']\n",
      " ['0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '1.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0']\n",
      " ['0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '1.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0']\n",
      " ['0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '1.0'\n",
      "  '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0' '0.0'\n",
      "  '0.0' '0.0' '0.0' '0.0']]\n"
     ]
    }
   ],
   "source": [
    "batch_x = x_y[2 * batch_size: 3 * batch_size, 0]\n",
    "batch_y = x_y[2 * batch_size: 3 * batch_size, 1:]\n",
    "# 代表从第2 * batch_size到3 * batch_size行，第0列的内容\n",
    "print(batch_x)\n",
    "print(batch_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "quantitative-horse",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24\n",
      "./data/base_data/img_11835.jpg\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZHCJWGZIaGf3A+vwSrTTb9ZqzV6+xsXD26jVX5xeMwyA/22is1qRUBGqzmlzAWMswera7Dh8jSSlCmE5ujAR4JDs0WuN9BBQhRkIIgl9Pb1lrRQxpqqzlkEgKYik0RlFUIWY/4YoZ0FhluHvnLq5uKBhmyyNcM6doR9aW3gfW2zUnxwdAoNYKW0CHjMqR2klVU2zNrh8pPjPsRi7DyNhv0ICZzShG8943vs6/+7/4n/N/+N/9+zz/9BNyjCjrMM6ijCHmhKsrNrsN3g+yyUmkFFDGcv/RYz78+jfRdYOuW+ZHx2SlqNqGOPRcnZ+R+x1HbcM3fvYb/OB7P6TfDtjKkhAYu3IVRhs06kuYvNzyQp5wTo0ip0iVPXG4psREGtekYcRWlfQ9lCaTCTGijJFS2mh08ZBGyIYwblHWMHrJeMiGNER83zH0O2IYSXGghIBzhq+994yvvP8eVdPSDyM4RyiZjGFMMCT41T//F/ns+ec8f/2SzXaHGSJRJWJMqGxZnh5iZ3PW48hF1+G6nl/61T/H3LX8nb/z/+Tl8+dsQ8SnCCnirOG4Pebek/skCyOZp/fv8e/8lf8B3f/tb/Pw4SOWZ+dQNQwZ1oNn6AdUDqRcCDnSGEcqCmscunWMaJJ2DEWTiyEWCDGx6Xq8Dwzek5InpMhi0XL/wV2ePH6KsVbw9xyp6xqFBGZbtxRbSTXZF1xTkUNg7Hquzy+I40hlNKauWSxqDudLXN1Q1TPa2RJtJUhvu8B6N2KbJRdv3rDdDIyjJ+aCNW563hLOWdCGXBQHywMODo45qGaEzcDFxQVXV1eMw4gf5VdOmewL2hrmJ6ccHy55/PgJn/zgI37vn/4TUopUWhFzgVzIOZBNln3E276CtZbOj2hAl/y2FzI95DklxnGgaed47wGFNRZUhiR7uJQs+3Hq1xQKVVVDgWEcOMwKnQpOKUpWpAz4gI4ZfMI1jgqNMZqUE0orjLOkMKCnfkHO0uNRWt/0dIw2/+aC+3+zVeSBDokwjIRuQGlNipmSMlmDx6OwRB9w1tKtthALoRvpVltKzMQhoLWm1AptLVYbSi7kXGjaGXU7u2mYaW1JqmCtkw5FRjJOpUBp8lQqWq3BaJSzggujBb+OBVM5LJpcCtZo3FjjfE3VNjRthQ0apQrWWqx1OFexnC+gaKyrAUMphqIsGc04NUYNmaHbkLxnNp/Lz9WakuSz6CwZb+h6bC5sr64JoePOnVPQoJwhGc2H3/4W//2/+j/mb/1Hf5PN9TUOzcHpCZe7Lcd3TlFGU80qQuiJviNnj6FweHrC/cdPWRyfcnh8D93MKabGOicNL+0hF3wYeLm74s7xXd7/2vvsVj3DtmdztWK+WJDbhtpVHBwfU1K4eXhSzsSU5FDMmWEYUMagrMJZRcqFyilUpcjZgyrkEoklU3SZshmDtoaqrihDT8qRUjIhZIHJjIYi99R7T/QeUsYoRaU1X3n/fb7y9AmkiC6Jkj2lRGL0FKUwrsFHcLblV//tv0A/DPzWf/mPWD3/gmHTUUrCtYeYSqGtYvADow9kDMU4fulX/xzvffgz/LPf+qf8g//s7/M7v/1P2a6vGMeBIY7U84ZEohsHtuNAKfDo2VP+yv/wfwQYhiFwcXnJZ58/Z319TRp6Bj/K4a4NWluCjxhtODh9wLDraNo5qXKkEEkpcjh2KALjuGG9eo3WgXv3Djk8WpJSgSLNdokXGltVzA8OaOYLtK1QxuGyhpCxGVSIjLsNJXooCa0QWM06KtdibYW1NUoZUlGMIbHd9ay3W7ZdTz81f0spU49QY7TGGIdxNUVVVNWMupqB93zx6adstztiTKSYyDFiiuJgecisbXHOsbNyYFYWfv4Xf5nf/63fxliFmhroSoE2hlQi1jmykgBpraWua4ahx1mDU5YcA6UoaeAag596adY5rKsIMaCsxTpLnFdooyloUslkpchJk5o5ySW6jWXbaA5UQRdpyqsCKheMNlTGUqzFGYPVhrad0Q87mrZl6LZYVVNKkgQoSzIJSDwxBhTS4P8J66ciuCttuP/oMaf37uKaFtc0MDEBtNI0rkYrTdECf4zDwMsXLzi9c4qxFh88BdBG0zQNRw/vs2xaWu3IMeJLwi5nPH3vGbvdjvPnL6mbGnQhxXjDfjAGjHU4AGs5PjllGEdmJwtO7pxISWyEZVKKnJ65KGIIxBj54nOIOeKqiqatWSwaYooTvqw5PDzEWofRlto1UDQhFqytqetASkWCjzUkP7DabJjNj2mqSjD2lCkklC4oEuO4o9utGXfX1JVhVjv82KFVQx8GbGX4U3/u32J5fMR/+vf+E1I/8Ozpe0StePr+e6Sc6MeO3faa7fqSsd9AiSyPT7j/5CssDo6ZH93DNQu0awgxsNleM3QrsvekEkm68OL8FU8ePOMbP/ezjJuBfruTz2EslEzlDCl4eVCqSprCpchDkhLDOLFxFg2PHz9kdXXFetuRQyAMHqWlekpTlhRiBuVo2paTh09Y3H/M4uiYZrFgjInee3b9QBoDVTOia023LSTfUVczPvzm13jw6D7GKJRRDP2GkhOKGTkFrHFYW6FURUgJVy/45rd/GWLk7GTJxeUbxrHn5PQEW2mMhZykwWnQpATJWpYnd/jTf+7XeO+D9/kn//ib/KP//D9jdXnOyfESrDC4xhBYbzecnNyhPViiqoqj4zsoY3n4wVf46rd/gRg8lkJMCYxl1w+4qpmqB40qSuCLrmcXAikG/DiwXV0yDmsu3jxndfWSobuk5IEQMlbLo1/UniEiDJs9S0zYOgWVM6REbQ2Q8GHAOI3OilI0btZgm5r5bEFbzahchVaKMCUiKSdiSqQcCCngw4BWhZTkIK+bhn39pk3N8dFdjo7v8MlHH3N1dUnTtKQUySXRzmacnJxwcnqHWduitOH15pJ+uyKlxNe//g2+9a2f4+rVc2rAZCHJWKMZY0/VNBRtefL0GffuP+Dk5ISXL15QOUPjDORE4xqapqVozRAikcIHH36d1+cX3Hv0CFNVaG1IywZlDKlATIlSMt4PxL7n6vw1eWE5OVrywaNvQFZYa8klkXNCKWHDpCQVU11XXF5d8/r1S46XB3z68ffxQ4c1cvjmoqSHhJIDtXKS2FQVv/7xH/zYuPpTEdyruubP/Hd+jQePHxMBW00sge0OhWbWNEJttIpZ03J9fU3Rmvffe4/V1TXdONJtdwzDgLN2Ko8Ex63rhspqHn/wFZ59+FXOz96wOrvEnGYGkrBBQryh9Qmupsha8/TJU67XK9o7S07vnhJCQBsrmzYKthwGT+9H+r7HT02vftyxGyzWKoze09Ysd+/ep64bjLZQFKDp+4EQEpWrAWkczuqKg1lD1orrYcfxrKZymqQyIWeSThQHUQWut5fo3TV3Hz2gNRCjZxwLu7HiervCoKFp+eYv/RK//1//U642K7owcnF9xa7fsd2uWa8v2K4vCeMWrTLV0V1O7j3CVDOyaVCmAV1RSiKMgWHb0a03dGmHzwOVqXlzec6DB49ZHC75wfc/4ouXLzg+OGS7WXN5dSXZuVZTEwqOjo+5e3TEsq7RWpMpHJ0c8ejRI85evmIslug9OQkEUpRk49pa5vMjTk7u087mHN+5w6bvaRZLZgeH2LrBp8S22xHGgXG7Yru+YnN1xm51Se3AWYi64GpDLgXvh6nhEjAK6qrCaEdOSiCeZHDVnLqZsThoqZq7lJI4OjrCmiIP68RiIiayyuyUh5LpxpHtODI/PODeowccHs44WLQoqylKAuA4jMSUWRwcgLEMKVEyKGOhkia60hqrAK2hKJJ2xJIZ/IgqmphgNUZW/YDRihgz17uB3XrNrhsYQ6LvRygjbVNJs1FACrQyaG0oU1XbtA2zeYs2ikJCG4sCfPIMcaDYIth+KTS1Q9UVB/ND6qrBaofREPNE0U1R9k30jL4jlwnjNkJxtNYRU6FgODw84d79RyjtWO1WFKcYiyfqzOLkkPv3H3J69y5aCcPI+4H19YYwDOhKc9DM+da3vs33SqIGdBZYt+TMQs/IpbAbA1VVcXJywnq9vkkcchhRJZM6TxwDaE3SmqgUfd/T+1F6PiWTY0b3Cm0Nxjgq66hcRT9VK7luOW4XHNZz7j19hLEVpUzVZ4zkLBVB3+9IKTGoQqQQQqBuG6xzBC/UVhAEMhY5fHOSRDKHQAjhJ8bVn4rgjgKvCquxI6SM8paQIpcXl6SQaKoGcsY5w+HygN1uS7aaej7DX16SrWYokqFvtx2mM3TasC4TZ7dyLO/d4cXzF5y9fs12t2PhakIK5JJvOLUp5Qk7M6SSCSmy3myIrcK1FZvNlpQnjjyKYRgZR7nYXdexW1+x2W4xJhOiFjzRaqHy2VreizEYLdz2xWKJQhNjoqprKuto6prDgwMePXyIb055fbnGzBpOZjUhZQKRkAORSDGFMY24MHK0mNE6SxcDu27L8nDJdtdhjcXnxOmDB/zCn/5l+vUaXVdkCpt+h7KaqqlpQotzUFlYHBxxcu8BPoAPim70KB/x/ZbriyvG3ZocPD6NeDwlK1TpyKWwWC7R1vDy9SuGfuDq4pzXb94wxiAPulJ87cMP+dYv/SK/+Kd+idM7dwSS0RofIznDvSfv8/DZB0QfqKuavu8oCrISnFObhqGXrHdQFWvf03WR1MJy1lCspjINph3IOjGzhahGhtQR4oBPI04bVBJ+d1ZCT4WCs3bKPjUhZXzIxARgqOpGSvGSSSmilBwMjWmIwdNvdyQfsU5RJo2FdhUZODs/Zxg9xhrQCls7YsjokumHAVtVPHnylMXBEmzFMDVGi9Jy7ZQhpMQQI7vBk7Mip0LfjeQEYfBstzs2fqQyhhwDm2GgH0Z83GfPov9IKaP1xDFHID+jDUzNPTNBF0pLZaOtUHF345Zu7EimkGMia4Vuatx8RtvMpKq9eailKo4xEIInhFEgrxTRVgt/vbITnqyp65YH9x9xeHDMqzfnRDK60nT9yGKx4MHTR9y9dx8fEi/PXrNeremGgTwE0jhAZTm+e8yDBw/4x//w/wN+oMQAMQuMmgdpwI+BXOB6teb6+poXz59jtCL5AXKiLgJ5jTFSjCEoOL9eEbXii7PXAlkZw6FrUUCMAjE5V0GOpDCSxo7rT58zVhYzP0bVldzLkgnBk3NCa0U/7IRGCeTNwKvPP6fkxPMXL4hjh7Oic4mpEFImZoGatIKQ4h8PWMYPI//s1/8rDg+PsE4CTy6F7XaH9xFXVyhgUWmcs5ydnQEQV1d88smnrDZrhlFoYkYb6uLocmEHaGtYmCXry0uuzs548flzfN9zVRRRBTLlBofPRQmP2Dhp1qnE+fk5R/4hq5dX0kABEVIAPnjS1NEevSd0PWrftEkWpeUBjqpgKksximIgzVuaO3e5++gZTb3kzcsv+OqHz7h7p+HiXLO8+xB3/B5WF3Yff4JT8OjuAwlwpdBtexrbYoulhERztGR+75iI4upyhQ+RJ48dp8d3CCHTtvJAH5/cIcTIkzFK4y1nUgxsVhfs1le8fvkJb86eo+q79L6icqDTmuvVG4Z+y9nr1zjtOFgcQ3WIWV+jQ4eyimp2hHKOVDX84q/9d/ni/JrPP/6Y6xAIOhPLyHbc8uT9x3zl20+IbeD1eI01d5ktTjBuhioeXQaOT06Y3X+IHwPztqXvdqyuL1mvr7m4fMP19QqUY9k4dt2WmEYOloccP7iPrVq2q2vwHWEYGQdDt0sEn9EUQhwxKkEEZQsWjc8OqgZMhXYWVAIVsc5io/QwgoJ6cYKbP6CPFp0HsqmEWZUCtmrZbDdTIxdstnQxcbVNbINm2/cYMk4VnK3JeoaqFYWBSE83brn76H2isigcBcmWc8qQLUNO7NZbnn/xOf1uhzXSx1FGtAhjEMLdzA9EXRGKxqPpoicMG0rYYYj4nPFZgSpoE7AGVGXBFpRuKGVGyjUpa4pWdM5Ql4wKnvHiNWbcYNNABJSqmNljDup7RAvBRqoqELUl5kJMkINCpQKhI42XGBWpa+H6A8RcUNoyXyy4e+8EbSLnZ1+gaSgxY2Lk/tF9Hp8+IIyej373d3n5/DmowmI+p8oRox0+GzpVcfT4fXbjQP/mOXVOaDQpIpFOG3wqrC+umdVzkpfK28eIUZkcAykrjNFQshxuKcGmxbmG1I/YYgi5EKoIBVIp5KIYUZSSRHsSRrZXG3Y5UX30Cdo5Qow4K9TlgjRrvfegFE3TkKJnc73ijbNcn58TfIcz0qBNZYK4Spl6JG+FkT9p/VQE91wy3dAzXywmjnskxDipTjU6JbTR5JzYbQfO37xhvpjzySef8Pr1a1FDTq+lKwVkqsrJTbOa5XJBjIGriwvGXrIrozVFJcFxtUYbTSpCz1MK6rpit93elEB54pQWoKRIygmrJs5pTDit8UXUfzlluQkpSxdFS1COKZFLZtY0PHn6jKPjE+IIL56/ZLmsefb0KZ988kNOTk+pq4Yh9FxdXnJ4cAS5TKq/S4ah5969e1TWUjvHw0d3aNqWy8srrq+uqdsZxhi0MihVbhSARk8lXiqEJIwK7z3GaJqmoh82vLl4w3q15ur8jOPjObvdNW/OXnB1eYEfBh49esby4IDgC+u1hlRQBmazGW07QynNwdExf+Ev/SX+8a//Iz757GOqpuX0zinNvOLo9ABb16y2G5arax7mRIyBrl+DThgd0Rj6UcpjhSaERNePnF9cslqtUa6mbmpiGAhjj1GaxWJGO2sFi29qipYmXCnSwJUMUSChnAuqJFGNTpx7rS2lMDEpNFpBVoKVKqUwxtDOFjTNgs1mRc6emAqgCTFiXCblyGp9xWx+IDh2kfueUibngrUGU0QZa4wEwJIzQ9+zWq94+MzinMPHMpXvQBFFcz+OvHr5ki8+k+xuMZvTtC2laIYQCElUlK7A4Ee6NGH0TP2AGABp/CsljewS46SClGtjJ5hEa4M2ks1rYzFGE4fE0HekGCZFbaZpG+q6wblqev4k2wdIOU0wwgRfBk+MgapWVJWT5mYucj1czaNHj2jahmHccXl5Sd0ekFNi1racHB+iVOY7v/d7fPT9P2Axa2nqBqNgt93SHhxzcHRMPVtQz2pmyyW7M2mkhjGg1dRfyJmcMmPwxAnH3zPg9oEyO2G+5Km6KcqQtUYbRe89TZZqqk9h+owCn1nrpHmfI1pBnFTcKWc03NB5tVL0/UCero82WlTPWa5TjEmS25zxOQGFVPbsMm5YZ0qJkvYnrZ+K4J5yZrXdcHB8CFjBZa2GqAHFmCK6aOaVw6eIT4HDqmK72ZBjQqOoq4q6rmnqmnk9x1WOXAp37t3j/v37XF1esrq85OjwkBwTlbHo2lAowopxjqI0qRQeP33Gar1m23V85YMP8CFKdjSODONAiZ5mL9/Xhmg13o/sQAQ5RmwOcpGOTpk4liEEvPccOcfx0TElSaCJMfP558/5M7/yC5wc3+Ho4Ij5bMbufMVus8IZRfA90WdePf+CDz/8kDvHh7x8/ikpBu7euYP3nqurS9abNU+OjgXjywlQKK2obhqZmayKsFTGIBSzKJRO6ypcVdOtdly++hTLKavVOauLc3bbNYvFIYeHx8wXB8QA61UrjUqVWMyWzGZzsoLBe+4/esQv/9lfoejMvJkxbxuuVhe8PHuODxlTF7abLevrS8KY2WxHlFXMZi3OVYQxkhOMY8T7yK4bWW92KOVYzJcUBd1uxPcdrp5R1RVtU5OKJjrLMCS8H6dml4jOlLFoJWITsWXIoIw8MJP9gJsYQXmCJbTRN1zk5WLJbnnE9fUFMfSUiW3i/Yh1HlMFVqtzHj9+JgdEFgqoMXI4aK1RWQkspy2pSHAdx5HLy0s26zVHJz2YBiZOP+IcQBwifT8QQyCFkXX0xOip6gZrK1xdSzZvHAQgCPxh0oa01iQy1hrQFmXtDY1XRHhC47PWiU0AkxWDUiglfaCUIjGI5YUxFm0s88UBdTOT92BEjKMnsVkI8YbCl1MihjhBE/I1Wk+kBGs5Oj5heXSMc45XX7wh5Sj4dPIcHZ4wX7S8fv2S7/7B7+GsoW1rZrOGN2/e4NBUszmHJydgDNZonr73PmeffH/q8RSUdiREgyI9H2nmV6XCVY5IhlwEjrVWBFU5AXIo58pSLebkTUeuHRlNqSyqCNMu5oJHAnl0YJUiNpqhFLJVKKclqUIEeLo2aMxbawGrMKpGG4cxjqaZTwfvxHMvkrWLFcZb6w6l/hgEd601WWfhiYcRpTQhJcYYsM6CUlRNJZTIHLlz/x4nxyfCj/eBEiPz2Zx5O6OqKqyWh9M4y9MnT1geHHB9dUVdV1RNCzmji0LVQmNEKYpSoj4bPd1ux3q14vTuXdrZTHw0SmYYR7quk0zNGgkcQIiBVRipnaU5PkYZhbGWEEXAVMjiCZGE2klMlJioZ4LtHh0d8/En3+Nb3/4GDx8+IsbI6vKKs9evWCxmHBwsMUbTxZ4PP/yAx48fMIw9i0XL8fEhx8fHjMNuevijlHkpUaZG2X4VFLlAjFnw6sETQsQHYayEXEAbdBm5fvM5mh1X1xf0/ZYUA8uDA+7eu087O6LbedpmTgnCgDlYHmKMAyUMAkXhwdPH/FuzP892tWG73rGNAeXO6YYtrmnYbTZ89snHnBzfpWDQVpgWJYExgv2mkIgxkxJCk6sdyhi6bksYB1IMWBeF9lay+PPkRBhHUvRoo7DOkqwFpIFqlH7r0aGYAs3e68NMZflenCb+H845ct1ycHhK/eY1fb+lrmekLKyHceix1Yzd5ooYO4xtBV+dKJ97FS5FoafgqFKGIt+/22w5P3/D8ugOtppjTY0qaqo6hNI59B0xemGbRM84yuHT1BV1IzTEulngssKMnmo0qKEhtA1Db7FagTESnLSaMs+peYvsWQnuk3+NAnXj0yIeOEobQkgYV1FP9OKmncn73CsmSyHueyzTdUxZPmuO6SZrLUpjXM3h8SntbE7d1nzx/AuGoRd9QwpYqwhx4KMf/AHDsOPg3l2s07x89QUhRJanD1gcHDJbHqC1oTWZR0+f8d2DQ2oydQtaWaxNoETl3cwWvPf+e8xmMw4PD/DDDo00K0s9YzafQcnEGPAxcnRyyun9B1xuOu4/forSFmMzKC0xIwQGH9isVgQ/EMeO3u8YuoaDk0OadsY4oQvOe4x+68Wkpr2hkqLveu7dOaauLOurBqPl2seUibmQcqG2FnMrqP/m2W1/xi+vn4rgrrRivlxw/+F94dxqzTB6YkzMZjOquubg6IgqBl48/4L7d+9SUqHf7shhwdgN6AJ+GPDdXhkqXOq6qbCV4wff+4i+60TxmCCHgKqdbPDpZIypYOuKwXu2ux1aa87PzzFkrBFGR0rChU9BzHt8CKzWa1bX11A1HBweUkqm96OwBXKaCK4iW44xMo49VxdvmDenOFdx/8FDPvnsB/z+d77Dz3z4Ad/53d+lci2mzpyeHuHHjqurc/pu5P33v8IwdITosdbwta99hcVyTopy8PjgMcbgQ8A6aert1bNFKXxIjN7T9yPdOBD8iB97hr6/eX8le7rNOZVLrK+vKFoxaxfcOb3H8vAYbVqKbmhnS0rWLJeHHB+fUjAiFnMOtBKo7XBJ0Y4+KA5O7nEneF69+gIfMnUIXJ6fUULk5PhUjNJSIsVIZYUqOoSRFAtau+nnJbpuxziIwo8S0VqhtVQqMY2sNys2m2u6viMkyZ7ruiXWDXGoyHFEF3lolE4468iom8+vlJKSPmVSFC8Xay3BGNr5gqpq0drSNLOJ+QDj2OP8jt3uim63wlVHEtzIOFfRNNJQV8VNVEtHjBE/BmIoaDx9t2McBi4uNxwfnVJVrQh2UiEEsb7QSmGUIsZADJm+FzHPbDanaWtc08rXxYgzGmcMbeUIRmOUwAzC/LWiEs1J/EsmLyfnnAhpjME4jXX2xlBLq70FRGF5sGBxcMjR8SmHx3d4dfGaqrXM4gzjhFHmvZAN0mR0l6cDdP+8KWNxTcvy8JiqbtluB96cnWOtIfhxap7LtX3x4nOqyoBKfP75J3g/8t577/Hk2XucPHhMezADpTAp8pWvfY2f+/mfR/uR5CNKGUraUYpCGUfVznnvvWc451hdX9LvDJVRk0K7pW1bUgrEYMl1YVG3HM4WxAD3jk5o5gtS6YXCOWliBIJKkCP9bk1FJvqBb3z7T7FYLsXkUCnS3vwsZ1JKN9Vi8RmnNHdOjtisTrg6P0SkGpkQEj5FQsyYKZP/Y4O5O+f4uW99i29969sTHlhYbdZcXl5LRhkjyYvCbrvZMG9nXL055+L8gqHrSaNH5Yy6hUFpo8EYul1H2YmDJDmjlEFT0FYwT4VCa4WahBVGC7slRSlDrXM4M3nBFQSPK3miRRpqayFngh+pXI01hmEMJB8m7P1t5j52PSVluu2aod/R7TbMly2LxYInT5/yB9/9HUgjF28uePzwCWEcQMPZ2UsW846mmfP8+ecoBcvlnKa20uiJke12e+OYaYzBGiMPqtKUohiGgcurK3bdQNXMiLHgJ9Xf0O8E912t8OOIURnvd4xjBQin/PjkDo+ePMO6GmUqGmfIRaNtxeLgmKZdELM0jo1zE785k3Ogns05vluBVfjo6YeO7WZF1w2oAqvrC3ROHN6xJAyzVlG7NGHhe+aGxrmaYewZ/UjXbzElQU5oVdAKjJ6UmatrtusVKUPRFfPFnOwVY7dCKYfSiUIUUYoBqxwpJkEEtZasciqZlZ4e3AlieKtEVChtKTnR94Oon/uOummIcSSEUTQUORGT6DUo6m01oB0xbiWjU5bgPbvthspZVldn+DFy5/QuRhthZPlIU9coVQihJycvFU1ONG2L0GhBG1Aho8hoMjkFqSqMxRiFz1CUIsZRREwIZKCVwAT7KkOB9CxywVaOnDO7rp8Spob58oB2vmR5dEwzn3H9g2uqxnJ6987UL0g3bqZ7Z9KSMyWLakppjTKW5eEpVTtHO8vnn31E8B6tHK6ei33CdsPR0QEheLRW7LYbttsNX/3qB3z7576FNQuUBldrScyD5fjkmEzB+57sBXorY0+MkcFHtKtp2xatNV98/jmry3NqK9CTSnaqpoJUNUA9m/Pq0+dcrLdsL9eYqgYdMNagrYN9T0tp2sahcyKs1nTbNdeXb/B+mHo9EjdSiqSUGcfxJmKVES4vLoljR/IDl+fnKPKNJkRwdyCE6bDlj0dwV4BTijQMN/hf8RHfdVxdXHJ1fY21jkplXr54weZqRbfdcnV5iUGhClKqFCYznQIYoXtpESZRJHtICIapJpVjUUAuU/krD3SZgqR4ySR0SoKdc8uqUxv52iLCo7cb+K1drlYTbx55rd1uR06J9dUFm+tzxtMn1GnEWMNiOaduK77z3d/jaLEkjjv6vKNuaygJRRIbgyJd+hhGrEHsSyer2ZwT2hjatqVpGjJitVpQ5L7nzevX9MPA8vAYpSvCODL0PUO/Y3V1Qb9dS0AokYhkC0UpbFXz7P0PODm9i7INRVX4UMBYMoa6XWCqBqUtrmpQWhOGnq6XIFS3DltXxFKo2xmzxaHQ44ZOVJ2TtW9SlmZ5LPa1rkKbasqiBUYJKTIGceVURdgvmYwfB4ahE4FXKZQSUWRcVeGqhqapUE7TNXO0cZQsr7GH5FJKDMNIu2gxRpz4pNJxcj8n6EYk4oHBi4K6qmpiKORYyFkM6Prtjt1ui6m2XG97sEaybfNW2wBic5tzIQZPJpONYhwGwjiyXC746KOPWS6W1HU9WToL5FE7x26M5OylZI+KcRDTN0lSMtYqKgMb30siMQzYqT9kXI01NWE4Zwwim8wT5z0lgUtEK1LQWmGtIafMerVis92SUqGdNcwXB7iq5vDkjqirx5HdbicUv9HTdb0Essku1znLbspWxfZZU7cz2vkC7WpQhsvrjXgd+YyzFX4UemdV1SKsqhyXlxccHx9zeHjEcnnA61dXHLdztGa6lx5tCufnr3n18Q8pIVGUpUoDKEVMiqwUwXsODg65PDvj+uIcZxU5BhqqCa/LKCWAVOoHGuPYXa15XX2CrRucK5PtgHjNZKCkRPAjhsSr55+zurpkMyYWR0ficxQjIQgZQ2s9WRrIM5rHzIvnX7CcNagSefPqixtILE3VTgFUkoatwDp/DDD3MHo+//4P6K/XgmcbRz96druOru8YRk/lKuG+pkIcPY2rxZpAKWGoTBJ9tJKNrCBHCehV5cTmuxSYsgf5rZ7+KN04PGYRj93Q0BSJsSSM0XJSTyKcvWNbTEkwzCLZvh9HuQlTpqJyoWj57912K00p5Xlz9pyjk8dUzQG+QFGZEEZKCeQ0cH72nPZ4jlY1JQe8F6MkraTSCX4gRU+MA3UlvuFaG2Zty3K5nIQ/GucUm/UW7z3Hh0uGbovOCa0zJUWiHwhDRxh2lOjJ0RNSoFhN5wPoivv3HvHVD7/B0cldVtseH6Io81AEbZgfHeGaFuNmFDTRJ0Yf2K43KCLWzTg4PGAxmzN2O5ytODg4ZlMKMYivTSmJdHVOG6IE96qiabRg706adYVCLtLoKiURo4cYiGHEj4Pc2wnvjTFQVxWubajrCjQ4J/7aWVlSHienCfFyT5O5U1VVsndSJhGJe18PJX5Bve/o+i3WGpqmZSiJUhQ5TgIvO+CHQXok/Y6qbQFumrYgCUBKohLNKTGEgHIK7wd88BweLPHjgAKM0jjrUGRUkSozRk/JotGIKbHZXHOwO+H0JGMIVEYz6ky/W7O+viKFQFVZiqmw7SHNbInygYuLjkyEIk3eqqrFzC0Lo2e5nGOModtsefnyJVdXV5Scmc0OUMZiXI02lhAz2+2W2VJgpMvLS66u1uRoJ5bRjOVyyWZtyEnYWbnAfHHAnXv3WR4eQM7UjSQlYexJSZ5HXwKz2ZxZO2e5WNDtehSF3a7j9aszgW5bh9Uw+MC425KjZ7u9JkUviVeJlDBVgsjPDt2Ani1pnaMzGoMYhelJZCWKZWQvBNAlUjtFDj22qdApowvEEqc4U7BOo2NElUSlFDZn/HZHaWfklAleGENKibWJClGqoypjiqLWCqLHqILdV/yTfjexd4ievPBL/EPj6k9FcFeACokyyM3QZGzKtMaCrTFZmjuNqxlNz2694fTkBGftlG0kmU+hnWRXQXDYDNI81RqrDeHGn0Gy/bd8UW4COnDT8NgPRLBOJMdaq5uAXorQyXJRMsjDiCAjhoit7M3PAKkSjDE3HttGZc7OXjA//JyqPQJXE6LHOMX6aosj0hhNKY0EvRzxfsTZSuhklSVl8XTvui05FUqGo6MjQBGCF+8QbVitNqzXG46Oj7lzcsz66oK2spjKSXAfDdfdljB0aDJHixll6cAoqnpGLoZn73+Vg+NTMpqr6zUFLU2iEHBNy8HxCcWYqVkH2+2Ws7PX7NYrTk8OOT06wqeIqxx1XTOfLyYq3ZxdiqToRaTR7wgTNKCN4eTYULdy711lxDNEC81u/wCmGDFO+MUiEJGszPsBWzfMJ4vdcRDaotKWmzutNG+He+QbbF2Cb5r6MEKjLKUQgqcfd8QcmDUOV1ekUAnzJWRhtYTE0A9TEA6kARpnhJ45NRe9D/TdwHJ5wLhr2V2uAE/wQfxTWs1yuSR4z3KxwGth/FhjpUrLkZwD2ogR1TB0jL5HqYIqAZ01JXn67YZ+t6VSGm0qsA31/Ih2fojabTm/eEWeGFugmc1mHBwcTt4vUonGmFhdX/PmzRv6fmAxdxP0FNHGElMGXcRhcYIdNhvZcwfzUxbzORrPYrGgrmsYB7TSGGs5PD7h4OgIYzUXb1Y0bctyecCr9YqhH5jPF4RhZLfrpVmP5vDwmN1mTc6w23W8/95TUvC8fvWGmDINgaHfMvYdzgrHPsRAo/amW+KsSMqoUqiNRedCKQlSphjBuYvaB1fZLSEPoDNjHGj1Qvz0b1rNBaUL0XuUBj0Ny8klk4YRV4SsISIyQRhMFL8eqzQW6Y3VTkPyWKPRSPLFPikt+9g0qaFTegsV/pj1UxPcSWJLWzknvGAfMIBDsPOcxYBnVtXs1hvhfWspB0uRplLRilgylglnm5SEMYaJaDpNQ5qwcybus5TdgmTpSUUqU3kmT4icpxggvHZdIMQ4QUgGVzUY424sOq0RSlWMhlSE224ntoRSUupuuw1nZy9xzRHzoxMK0vxt2grnDFrlCZtL9H3PqILg5yAQgBfFX4yeofcEHydsGF68eIGw6AQ/Pjg4QmvNbn3F8dHBpLSspFcwVlTWcrCYo4ujrjSLkyW2rqmbOavVlgePn4KybLYdr16/5s7d+1jrGEJgfnDI/PBwKk0j203H888/Y7O64t7pIXdO74jw7OIMVTLX19c0VcV8voAiGXu3WxNiACZzsDxxepXleIISjNWgCj4GgpckwDhHHHtiCPR9T7fboWxNnryuC1A3Nc4ahokjLBWA7Dpt9ASnyYHSNA2z2QylEB40ljjdgxgjIXpiHLFWM2tb6qqiVLVACCGSYmEcPdvt9mbvdX1PdXIgDowT530YR7bbHY8fPyCPJ1xcrRknqm2MkbqusNbQdTseP37M+lqsNepaejrWWvo+TuwnccHs+x0xBpxWpCiqTWk6eyorOLt1Ne3ikHq2IFcVJcs1cdYxm824d/c+8/mMz1+dUS1qFss5fbfh8uqSzWaDMZq6tgzDiC0KHyIojfdpcnGVgOecYxwHtuy4e3yCc47Dw0NOTo65er0i58x8seTu3bukAhdn13zx6We4knjy5DHXF2/o+5Fn732Fs5cvuTi/4PDwiLPXrwGYz5ccHByyXBygFfhx4OBgsoewhdVn38MoCNGLDqNogbWKPK/kPDlbGmpXoQuUJGE6qiT9NSMHnFIiPPQ5igBRZ7STz6wRLYR4PiHq4wyZhKscymjiMKJCQqWEnqzFZbKUeMYoJV4NsQRIkeRHqB0a0U3soeD99DGZRieB/WaK1I9ZPxXBvQBu3jLmRCkizVXGSHNFK5QXOIWcqbVFo1DWgDWkvXCiIKUSWnxBYhaPaBzaQxkyNouRlZCaNXp/CuYJz5rwXWP3wySQoBInJ8OSKVoJzFIZnBIHvuy9UPiKYGloSzGZXNVgtAhedEHpyM6vqauntM5ydbUC8zFfaRzztmZma0bXYHQmK6F49pstxnqsaylZE3zEGmnAxXFEI2IHwaKlZ5AvI1obhnHEaIvKns31G0r0aGDoxCpgu9lSVRXHBy1KtVxfX1EUzBdPObl7H1DEeIazS7qdZ+wGdqsND+/ehzASx8xsdowzDSVlfNjw4otPefHiOU8fP2V5dEo9O+TlyxeMuzVN0+CHNdcXO05PTli0LW46LMd+YPAdcfR0MQtsYTRN66jzTHoqKRF2PTomamNo5kcMY6aPic12R/YdOkdKDgw50RZNXSpUkBGEuQSUEhuL4MFkQ2PEcyTEkXZ5hG0P6LIm+YRxViCXEFEx4ncdMcJ8cYytHAlLMcIbzzlhUkGbzHZ1ReelJ9Kvzom1wCuhGIqt8TkQwgprCvOjZ9Rtz/ZqRU6aGBJhmrg1hpHej2Sd6foVhwet/CwsUOEHYf0wRtLVCoaRczUjjh0+DNgSsERCKpjkaFzFclZhbGZterJT+GBp7YL5nYfMT095c37J2fk5Dx4/JQyK9eqKfnvNojXoHJm5A/JQGLxi2CaZBRBGfD9iiyXtEk22jKsVdhaJM6kiFvNjjo6e8erlG5Q94Gh5wtw6Pv3e9wA4+/SHOGt579kzHj19j9EHxm7Nev2Gly9/iKsk4ZrPlpycHHN6eofF4RGLg4a6XVLPavoRxjFz+vBDfvFX/zK//vf+Yxo8adyQTYNBoxMYBZpIMYloM8kqdFY0qqLPPWnq4YkVdabEjE1KLMTHjIsaZRoZpFGEQrrP8qVnp2jcDF0cjB2EHVoZjFHEokh6Qg2sZhx7jFYYLfz40Ud001KSxuiKmKP0kqaen7TwvjzW8setn4rgvqeepZLxIdxqjE78W2NkHmHOxBxRRlGUCCByKTdWsjmLwX1CGjazStRze3ZDAZi4zcCkTtTyN9McRLKIO6SRMc1GVNOpbI2U6jlNtsEy67SqK9rrFh0sy4MDlodHbMeBcH1JGBNgKCoSU+Ly6pL56UMODo/Y7kYuzt9wenoHq47ww8B2s2U5d4xjQtta7H2zIhdNLqIuNDqIqZYPGA3YyUPHSKNOGoTiwuiqWtgJCjKF0cskH6U0ykAqibqtxRtjgq2sq+i7gf3UpPVqxXa7YegHgu9JYWTsM1rBwXKJH3soiq4b+OyTH3JycsrJyRFNU/H69Wtevfyck+MDjNG0Tc3nn35C5Sx3T07RSuGM4+rykkKk60d5sEeZj2ut5cHDJ2Inq2EcOlRKWCeYd9O09Ks1w9ATw0DrDEpl/DhOI/YSxiqMKoxDT84RY5W48xlHUzVCG7VOePtZ/D+Kkfvrx55hu8H3G7abNSklFvMZxqgbiqu2Yu2cUkCFzGa7YRwGGmcZtmu6tqJ2IhCKZeoBkdGqsJiy0NVmRwpR4CU/Uqbxh+v1FSFEVutLTk6eiFajcuw2aZq2NNliRy+Zul1AjmJfm0WVGkOgNK18rzUU8o3oiJhxVU1d16QY+OL5ZyLXL4nzs9e8fP0FxJGDwwVtKw6GKSbeXK1ZHm0Yho5+iJScGAeh1Oapl3PRbWkb+dx1NWc+X6C0paobhmHks08+4aPv/4B79+7hh4H1MLCYL5jP52w2r3j+xTXb7QYfBnad5/j4mKoWBbLg+AcYW4RRhyVGhR8TuprxZ3/tz/O9f/abvPn4e7RVRcx6GsYzNSlzkjGMZNAKZcRdU2mHKVmYR0qhUmYi+GCMIycZLqS0oUxydmUE3stRhGNaWbFM0ZqUZUatroTdoSatSWbSDRiDNuIEW7cNfbe7EYnlJHMMBDMWxp0u09zjUv54wDIwBXGlp1FsUFUyKLvvB8pk5lWUlOshJYZhkKEa0xDpgsxL3Gf9SmlM7VBOuNe6MlCm0kwJBJOKmBap6UbmLIIcU1W4pqGZz2lmM/kzZ7HOyYxOiow+S2JBQC68Ob+gT5HBB/QwEEumqhuU1eQSMVroaTnDbrPl4PCUu3dOOTu/5Pvf+wMePXoAOWDtNGgkBrrdBuOCDE+QOgJvzDR+UOTszhiKVWLNip7c5uTavLUEUJOE3lLc202hJ2aItRYUtLOZUA9TZLu+nmCExPnZK+JkVNQ4S7dds46ReVvhdOH1y+eUAt2uI0fP3dMjVEmsLi/49NNPqSpDdV8ocrPZjO12yxdffMGTR4/k5zVFslU8OXu61BH8yOrqEo3iYLmkaeaokgljj0ojNDOs0bRVxUXwqBwpKVByROVADgMkT4g9VVWTS2D0HbbSOFWxoqCswTpH1w84U7GczckhEnMHJuH7HVfnb9AloZKn21xSkqedNeQoE6xSChPPXhNDQKVC9AP9doWzYuB19uoFz54+Zjlv2awH3DRsZBx6qmbBYt5iFDLJJwa6zYp+uyZFT3lwT4QxoaNkGRlonaZM9rEpeMiKojPe9/h+QwkDJXkqq7BG4aMEK6sRpXNMVLairVtShMpooh+5OH/NxdkLDo9PWV+/5uLyildvzjhazmg01G09DW7XoAv9sOP1q5f0o0cb2G43vHj5BRSoG8fq9RXrzTXOWnbdht1uh1KKcfS8ePGC8/MLLi8v2W63xCh7crVaEWOUnkOo2O7WjIOnbhwxJKyxVFV9A3HGVAg544xUR8NmYL3Zcnz3Ab/ya3+ef3B9xbC+nOJElqq7FPGHSRmlZfSkLpEc88Rdnzj9CooW0d8QIrPlAp9BOSdUziLixqzKjRp9T+QoRlOMJqtMVqLqFbhRTew9EeZlhdAclUY7N42/lB5iBpQxGMRlcyIE3mD95Q/J3n8qgnsBYohopUU+XUT9tVfEGScDM2IspCkD9SF86fSSTrICLRdJaYVtG+r5DK0U88MDTC+TxY3WkuFHoWVlpCSqlKKezXF1jWsb5ocHNG1LmqTSYR8kraOpKkouOG3QwPLoiGw09x485OTOXbZDz8X1JWMYJduNMlasH0Y2mzXz1TWz+ZKHD+/z4sUrPv74h8zbCqujDJzInuIDxiVm2kDdSjMxJZGSa4227mb6y56f7aeOfFXVNE2DMeaGfpYmfxE1bQpdyiS3N7fUmULxrJCqxvuEIqKJGAWKyHp1gbWWw+Wc0Q9cnosfe4yZk+MlQ7+l77YEn0hxpF4ecX5+zn4c4WKxkIacEk71OAzMZjP8uCVoTTQyFcuHkYs3r1kuFjx89Ji6stSVZn25oywqUadajVaF+azBUPBTczgOHaoEYujp+5HN5op+2LJonThCGnVjFZFiYt7MqW2N7zsCI5Etm/WGy8s3nB4tyXFk7Ff4mGhqQyni8qcUVJXFVZYYRzKFmCJX52e0bU1lFOvVNXE8oakc3jmMUpQcWa+uaX2i5Ig1QstYry7xYWTo1qQ4MuzWrFdrvN8yjBuUTsKUmKY/5CIJjrYwjDt8tkTfYfIodFBrSF6esuhHNtdigJeHQO1qvIukGHnz+iXr9YqSRlLY8fzzH7DtOsIY6XQi6ExVO4qWDPb09JiqsVxenOFjYrGYEZPn5csvJn0CnN45AhL94KdxjoE7d+9inWMcR6xzHBwdEkPAVQbnKubzuVgBO8t8MWO+nPHy5XPW6xVV09LO5swXS9rZDGMNxYhXTdUYtrvCGBIhQxw9X//5X+KTH37Ed//5b0Pw5CxEC4qw63wBXdeYuiaOYjKmjAPEb0pAdaFEZmOwsxndbkDVNc4ZyHGq9ItUiMahUhas3xmK08Qk78caI4cxaqI1CuEjK/HxUc5RzVqKNijrsHXDOHRoIx4ypRR5P5P9gEb9YUzIn47grpXACaXIaCwfI9Yq6SxrjXHyAI45TqepjKSSbL1QVRV1VU0YWUI5Q+UqZss59ayVwdYKQpHh1hmhPzpjxEBpkt0b5zg8PaFZLjh0hkfvP2M2m1MmOGaP7yutJ0MkGSYx7jqyhmKtOCNqjakq7t5/yBgGNptr+t0aciD4nvV6xWJ5yHaz4v6Dx9w5PeWz3Zbr60vaSlGioeSRqpKBBKVMvG4l4qw9zqeUbJ6YI9oaYhQlqrX2JrDvA6pML9KTUdQk0tFCgVNKYfcVUCkYFCUJ3FXtlY3IYWC1oq0rrDGiklXSoLbWiZFZ3RDH/mZweFNbUvRsdyNVJQrI999//4b3r7Vmu91BKbRNQw4yYacU6IeBEBO79YrLumI2mzNvK9ZFILsYpIdQOYczmtXVBRlIfkSXRI6e9eoCrTLXV+ek5KnrGWFSC7q6QlsZ8dbULd1ux2rbkY0lxMTl5TlKZVKrWF2dk+OIc9XklRLEWzvJnjTOUk2DF4zWXL55jbMGVaTCub66oKkd9Z07mIk/Po4jIWRyjDx8cA+UZru5ptttcVaC/Q8/+i4FRQwdr199MQURzYOH9ykpMfYdMQRQmfOL1yTbUVmFSh5K4eT0iDBvsVVFzpH1+gpjLJVSHB8d085mE19/JOfIYl6h0sDmeicio+WBVBXBc3rndBr9ppgdLKmaOVlbTNTgRDhnJl78HhJs6oamFSuGruuEforsy4ODQ7FVGAZKEfKCqyrmszlNK8ywdibQ234PHx4ecHh4yGw+E8tglVDaMgzQDTI1KRQw2nF47yG//Gt/gU+fv8SOW/AenaYEScPB8THtckEpip29oi4KKg1kqcYmwWI/BmYHR5w+eES53nD/6TNiGokpTCw9yaZjECix6WaoUlhcXTLudlTzOfOjI6oM6UbRmkkp4MZBektNRR0XtOfnzI+PSWS2a4u1mjz1X+TaCfi+j30/af1UBHelNXXTkhALAGUMVduKlF0HCUpVwRjhoGI1d+7enUQBgZIlcw0h4JyjahsWiwUnd+/y8Mljrq+uODw6ZD6bTbh7IcWEmoZ9ZkQB5tqGOw8fsO522LZl1XVs/EgoE13MSG9gn+VabSAmLq+u2W57Dk9PWRwfY+paNnkuOK1wo1AWRx9IPmEJwk3eHtJ3O7RmmlYkTBg/erHbVUL3KxNObIyeTJcEmolZRo+lSanpvRj4z2YzofRNHM+3TnIT3rjvZ6i3s01FWKKmznwWfxW4ERDpiQ9+cLCkaRrGcZyMyeTvnLPTa4lVqgaBCp2jKG4OG601BwcHAOQY375OAWcth4dHYm+aM4dFWB5lEpV03Y6qcjx+/Jja7oeEax49fowxhu1mg1IwayseP76Pspqh36JI1LXj7p07TBPKePjwMc46jK149PgpCsNmsxHFppGHr7YybSuMHbOmonKWUKTqwxj8KJztds+cmXoUSlsUadJTCAMs50zbzgBhU6kJBkxkqrahnVhZKWcgMWvrySXQY4xl1jooMiWqXixkOHSKLOYLYvT0fU8uEV0CbTVDoXCmoGZCpzXWkTLYYSBO9sBt2zBbLiYusvS2UhY2hi0CbWIUs7ohOo1h2o+lUNUNVVOjbSXZ6eC/5KGTJwqy6A48zlmOj4/ox3HCrwWXrhqDa2qCFxJA204HkSrUs1YCflPfCMHaWUs7n09DLSwgQ9+7QfZiiJG6aRh3iWgcz37mZ3n01a9z9v1/LvRZXXB1DWSSMSxPTth1PeO2p1KOet5I0JbGA2PfY5oWXEW1WFC6gSFnsIaIJEjaSgIWswjqXNOIk6xzZGtQVQVWBHE+ygFU8uQ6ZSw4g65qsvLYZkbWBlu3ZLUho1FG45RMfFJKT70WMQX8SeunIrg7V3F6/x4hJpmKAmjrBHOKEZSavNqFbqWMvhncYI0VP/FaVGzaaMSkWlHVFTEn1us1m83mRuXHhCXrohl9wFSVTF5xlpdvztB1zdGdO2RraBdLFnU1uQROvu9TNqxyYX1+yXa7o21b7j96jGtbUIpZO8OHQOg2JJQM2ojSnAlDT9Xt6Psd6/UV7WyBtQrnNDpHodsZI80/EnkamzZfHOEqK+Kryd85s7e0TYQQb4Q4e3e/ifc5ufXlqT8xZeJTQ4i9+g1gUtYqEBl5kXmTzhjMNHdSTZRCV1foKJWMq8UELWXh4urphxQQ+tkUpPe0Q2MMWDepbcNksWqpKicw3URlRBi/ky+J9AnqqhLmg5Z+ia0kKKqpsjGVE0vgrEnTtPiqMqhSU4poDWgVRhmskbGHOU3Dt5UMVVdKZuQ6q2Wsmarl4U1yKIXpULR2os266aJKpASlJ2GZHAYFCYhlMiLTU3murZtosuaGcmmmCmgvUZc5ntU0o9RJ9kZBR4uuoZQGY6UK1aZisZBBEt47cgwTlVd6Odo5hkGsEXTlJlsCgePk4N/bIyPUW1vR1DXZWZjcE0GmpdV1K4dGyiRbCV1Z65t5n/skYq8fsNaSinDqnXOTaErsthXmhjhhjMU5hauqSdkrFadzDmcrcfjU0l8jixaBnLi+vObNmyvaRjQYmkJbNfzCr/w5/nl/RSUbC601ffQ8fPqMR8+ekjOUMTHTFa5x+BhIUdSjTfDU8wWz5RHvf/gzzO5e8uy9D3CNI0y2vxLgNd1uy9B1JO+FxWRrLl+94NF77/PwyVOyMmxHT920kwpavJ3appaegpe/mzlHGAba+QyrhHfvo5fgXiZxpZKEio+/92Pj6k9FcK+bmofPntEPg0zjgZtyGa1QytAPPTF2XF9cMA4Dh0dHooCcKpM8nYR+6AlZfKDnsxl+GDh/c85uK5PCnXNSPhpDDIXj0zv0fuDxs/cYcgRnefD0KbapKcbQtK08TFNwSiVDFIxUpcJu29F1A0cHRywPj4hqmrpurShWo6g1Ry/WtSVHjEr4oWfoO7abjWB1Csa+J8eOtlZUtsaoIswP36N2Gw6O71AbGamW9lLuG1WmWKzWdS0BYJLVlywPqVJfxujUPrDDTWBXWpR6e8WEVuKbo61kcXVTTyPAAsoajHKkm2+exrRRhGYqPwXRw7xlM+0hIKWEYqi1luDmFNZqgTKUwsRInCyL94ZtaZpQb5TFGXdTKcQY0fvJ8WYfXIqwjJiqCTLsm5BRBEfO1tSuBQU+9BT7dtK8RqOVpqor6rqSbDNGjCkELQNZxDpWRqyh9uMUJbjn6VCtJrbWTSY7XRf5vxIbYi3Buug8DZt2VFVFSvspQsIOsrZCa4vCTLRXPzGk5NCT4Q8WVzmMnnQfwWKVVCLGGlzToK1l9KP4y2uBQipX3eyjFMvbXWGqKYFyN6xqhfgXOVtjrSHpjC8aa9TNgZazjIPcv45SBsrkW6MFNrXWsU88tLbSLK1lXrK2edKbmJtD3tgaEJ/0MAkVayOiw82m59Xzz0lFc3J8zNDtsLVUJh9+6+f43n/9D7h49Yp6Osi3foAXL1Btw9Vmw9V2S180/kJG1+lpCpWPkbTtaHY9ennA64srzGxJmvZkLFEG/igIQfyrcowczhdEZRlTZrXbcpISSSmGEBlSNzlySuW67TvqpsIoRTeZ17XW0o0ejYxxjNEL/BPLTZIa9snqj1k/FcHdWMvByTG662gLJAo+JSnJp8ZDCiMhJN5cXdJP/teXlxckH28emn3W9ujuI+q6oeRMt9nhjOX4+FioddOYOxE+1cSUmB0suffgPt/9wUc8uPuM49MTVn2H0kr4ysXcuOUxeceULPTJEAJOGw6WB2g3WabGTMhJgvyE5Vd1AzlSoiGnHX3fMY49fbeTTHvC3TSKppbgJPMaAzmBtp6h79GmJhcz0T6l9GfC30QgJZatewVbyTcxXCik09fB26C7F2uIRF68dZSSh3U/NFnw1AqUJhMpU/NJ28lLZ2Io5SLfq7SZaF+TRoG3GbubBmCkwtvRg0rjXCWMJJASNScxB1JKhguXKfAqJ77sSkFOWOPIWbzRpcEqMJNFA2ZilkRSklZU0ZlSFFU1p50tyCVRTMEkGRxCmVTKKJyrMbaeRIIRlQuKgCqy51zlcFPQUYqJ2aVu7gHGCtNqf8Ai14ipCjHW3eCnpkjVYKwcsRqomlrUs4hHktEVSlmyAYVF6Yw2kklrMwBTxWAdTmnQSSwFJuthBVQosFPlo6Qxb1wls1RNImppEsqBLwHWWisKS7WH62TAiTEWrYsMG78F84UQbg72fea+b/rLHAQlv4pUfiihHBe4oR7rKbgL+iekhpQLKkmFrwzgE8ZY+o1YQJ/euc+8beD4mBACr96cY7Xm8Xvv8ebVGT5l6dFZzfzgkHsPHjBrWmxWlE5cXK01aCVTlobgSUrj5gvmB4fUQ6CaL4TQkRONNaA1IXqyHlE5YWqoZjOq2QJlZaC5TxnlQDkr9zwGVNbUWlw+lQGrFco5uq6jsk4QhSQaDWWsJHQ5vB3C8ofE1X+l4K6U+gTYAAmIpZQ/rZQ6Af6vwPvAJ8BfLaVc/aTX8SHw8Wefsuv7KVvXxMkNrUwl/Wq9plud8eLVS3TMjAg00MxliGxVC7Ry9/49fvYb32Toe37w0Q8Eg53NsMZAAavfqhLHBI1ztEvpvrfzGQfHh2hrWSwP2I09zllcsTeskxDExnTWtOSYIBdmbctiPse6moDM5NxL261zgrXGgHeWFAbCKN7WWmvW6zVaGw4OFrRtix9kHuusNihg9AHtZjRavEfKbjcZau1VDZNzXEpTOfsW8wTQU8MUhGW0f4CAyeOjTNx/RZkofZMLk0AhWg5fW0nzsQDK2slcTTJPYLpP4nyIMjfBXuWC0kmEI7dsHeS9TYMwnMNZN5mOGTFWKqBMufF+EWaI4MEKi2Kav6nBKIvVTiCVPdK0f3+IK2aePptMXyqYumIxP8SZSmaqNpWUux7ZJ8aKTN5UGCOZeykaY/JNpgkCHTgnf7/XDIp3iJ5w9TxBD/amWooxTfMG9hOJZD9IMJ36FRQUTnogKVBSFHWqcShdTYHTENMkzKrcTeCU6UkWpywYqX50SpNtw0TRrSa6XynTQSX30mBE3Ie8f4MiK3C2IkyYr5ruZeUqqsqiUAxBIKVSCs5ZmsbdgncA5NBzRujA+6BvtJ2exz1mL+/dTpk76CmB0ShlpoNk6j0paKaq6sH9OywPD9G2ZhgSm82Ws7MzxnFkuVjy87/4i4zbLR9993vMlnPwoxAKrZG9bSyBEbT43edpzF7TtnQxgtYUrYXNYjQH8xMG73FNRdu2hOhFC9J1zOqG5WxGSort5SusNSwPDtF1QxUCxlUCoanCMFXwdT3NTz4+ptea09NTVpeX9N0WRRZHzxAwGMHrp0rwJ61/HZn7XyylnN/6/V8H/n4p5d9TSv316ff/65/4JpyjOTyE2YKYyjRtXUyySo7sNmsCsNl1tFqTyVRaUy8XVHaaT+gcicLJ8RHt4ZLN2ENlaatacLkQJOM2QqtMOdNMrIiCFO7FOLKyxKJlkICusbrGTGdkVgrlrDRyrJUSViuiEqc5pRTRC6abUpKmR8koDaiEcUUon0VzsJxTaRjjSLd6gy3ibLjbbZjPZ+i2JZFJJWEo6JIJfU+J4OrZRIEEKIjVip5wWSccWER5UdQEZexxb5BqexqlduMrXRSVqW7GGQrNVEpqZzSVNSglOLwqWRg0RXojdsLjAYrZBzjkXegiFDO4keCXkidvcI3FgZE9UGsHCQaboTYUxC2yX3XM6gVa1biqJeURsp+CkDQCpYEsFYJ47k+N4AkSiHHqM2hxC3WuwjhAiYOkSlO2bOuJty6HYlGabCRo2j3uHDUlKrQyE53S3GS0ExqDtpK96zIN6NCS6SqtqdxbOMJoM1kcRIyxGK1vPOWVkgqxYN7CUc6gtGDjkIUmPAldXFVNB5MWvUQBqyQQJOm43EB0OqqbaVOK6UTUEoBLzlMgtpCkAayVQrtJY6Hk81TVxI7JGV00djroUVCUDCFBKayeKLaqoJNCW6mgrRYhmKJQVxZjLdXEo69ce9M8NFpgEmvK5BNlMcpCMexKJsVMjJCoyMnQdwOX5yt0MdS2IcfMuVI8/oVf5rsv3rCKCYol+4z1GVsKQxSLDzeqqb8kiUnMgcY6QhyxCuaNRZPwlWEXMnOjZWRjKRQ0rm5k9qwXIZyoV+V/xhqSF9gHY9h1W0EQ6orcVARrqA8POL+8BOeICoytyMmL46rT5DRN0KJMlh0/Ia7+xL/9b7f+CvAXpv/+PwL/gD8kuAMUJQ2/lIXP2dQVs6Zlu7rEaU2/3RH7EZdFRdZOTbUUI9bVuFqGcjSuRjvH8vAIY19KEzNGeUhKokz2o0oX8phlCELX4VNEWzuZgU1q11goJAKTJcLUsUfBOIykICVS13Ws1mtYLoXTSyYkkWaXLDa0aoI7QpBNMpvN5UbFgI8j61WYNrC5aWyiFMbKRrtRGhYt47isnYK1bKq9YvZ243I/TQcQsUWSh9tNask9VXKfwe2DTU7pBsJSSkYY7qmSZsq8c86CsU8si5ts3LzNJkrZO9iVm4MI5H0Za8RYKSdR/GYNJkuprTPKCkOm33X87u/8c957/D4PHj0jloAx+kswlJqCTc5TM3Pyz98favs5mexZQ6ip7FeTgE1jkExcT9dhX+3cjMdTMpZPRChCuTH7IdVK3wTNG3cLM6HrSk0NUIFk9q+llEZPcJdWTAOnJ2dT83Z6Vlb55r4YI43EvTtmId/44+wzYe/DTTWUUiLFcrMHzTSEQ2AQezN4Zl/JukogsTQFd2ss2QusU8g3ENrNz5uotUqBdYYYRXei9vCbszfZeynC8IhRWCiqKJy1Yim898uflNT7KltPTJT9a7xli+1xeoNtjNAbhyTujGMgBFEBW2NkvOb1jsVRRUiFr37zm/zgd38fTabbiR/RvuaKRVhC4zjCKMZhsSTqxQJfROHMpBcpyIEZcyb7kTh6KFk0NHvGmZUh53GCSPfvedwP7sgypzXGyDgk8coKkdV6TXz4SKZeTV5KavRCiVZvm9VyLX/8+lcN7gX4T5RSBfj3Syl/A7hfSnk5/f0r4P6/7BuVUn8N+GsAx0cHWGeplUGZLEN3rdAP9m6KlxcX4MEWhzMGWwxWadykWEOD0RXb6y0XlysODsRvuttsSSGSQiSkSBUTM0ldxc5AFca+Y/Qe772wE5IoWXOIxARaZ5SzaHPLs70UjEy+Joyevuuodt3kkidS5BzEvS/6QAoBP4zEMdC0Mplns9kQYxCMTwsrg6k5I9iflM5am6kaiKAmIzPBCchl4plPD91thsL+F7zltr99MPTN1+3hg/3XvZ2f+fZrb4+hu73Bpnt5K2ipm9e54ddrUR7v6ZlvewPceFo3TYNzhpwDhkKSKcM4bdltthgN7cwwTpOXcpJA1TTN2+rjVi9hHyj3gi3jLLq8DZrGSHm9vwb7Q2J/yO3XDeOliDraMOHqao8574O/BHCBvQqo/Pa6sP/724F4yoZvXet9w3kPAb5VFhu0NWhr0RONTivYf5r9+y7/koNpf5Dv78f+oLP2LTwp1dxbvHyv5dBKYytzcx/txJba86v3P0u86blFuX17YHxpf2iFdVItvoVjyg38sq/AjDGUzJe+921Qf7vHoOCDOGYarZnNKkaVWD+/ZhwHQvDsug3j2BMuhXL87Ol79Fcrnn/6Qxl3OFkd13Utvvc3jJ4JvkxJni2tSSlRWUtJWdT0Sk8DwwUiMtZAKYRxJIYssC1KiBXeU09MJKP1RHkVxp8MEBcxWls3VBPurpQiBHFT1UpRciL6MDXuBUr7SetfNbj/26WU50qpe8D/Syn13dt/WUopU+D/F9Z0EPwNgCePHhQKNzL4qqrFLz1lTk/vcPnmDKUUTT0jqoBFAm9RiM2qSljnUEXT7QbOzt5I00JpumEUVoYSTwcfEgwyICOOHlAECoMfyTnTbXeMywGtLWn0YAvKKrKKxDApY7PcuOQ9w65jHAbJAC6vhBNPoet7ur4jZY/vd/hhgx+2pDBytHw8PczpBr+s62rKepjKfzlEtNGT0X/CqHzzQAt0Iu6FRiv0LYw35z1eyZeEDlq/zbxuP+xvM9PyNpjot0H6dqD50V/7B++GennrZ95MUYKb4AnywO4HYuScb2hyGChGzJgshqLF2rhWlsVsJt7jtpCiNHHlOrwdALEPLvtgZqbf/yg+uf/am4MPbqqr/fu7/fn2f2bU5CqZ94fh28+v1HS9p2Yu+u0BS9mzhr58P/brRw/H/aG5f58icefm174RmbMMitn3MbTaVwjcyhT1zf3b7wk1fY7bn/+2zkFPh6JUu2/f4w1OfmtvlQnaKVPD1tyqOm7vhZvkwE1B3LipctzvQ32zN3PO2GlQy9vpUG/3z+3XttaIHiAnVCpst2u2uzWr1Yqu3zF0HahC6BMnh4csFgv+9K/8CteX56iJ/y/OoRVVVWOVuRGZaaMY/UhV1ZRK2vNNXcsYSKWojIGScVrjKunBKQq6SLWEk8PQh5G9s6wC4rRnU8qg0nSgVeQURUh5eEQpmfl8zm51Tds27O2Ky54OOR0SP2n9KwX3Usrz6d9nSqm/BfwK8Fop9bCU8lIp9RA4+8NeJ6bEdrtFGYcPmdF7CaAhcn35hpcvX0iD4eyKIWWcNuLdXFmUgm4YcTkT+4Fu6PCba95cXHJ9ccnq4go7Zb56OjET+5JpCpAKkjF03vPq/ILtrqOyNWH0VK6huL2fd5hEU2K8n3zgxefPGbqOfruj2naEVMgK/CT1zzmQ4oghUlJGAYv5XDjsRpOzUPWqqiKXNPGmYc+AyRkiUSaw2LfBdl//S3n8NkO7HZime3Mr83n78Nz+uv1D9fbh/HI2efuBvZ0V7rPaH60ObgfJ/b/d1Djc/+x9wNmrVo0xpBLFNS8rFBabNCk6ylCYuRarLEHFiWUy+blMTbz9e/nRnyvWqEyw1a0KZH+gKIW6FUxv8/BvX8c9xKW1OJLK55yqqlLeBvcpGBr3NmDntO9f6JtgJw3BSVF9O6jeujY3VZOCZETMIurodHOosW+KTsfd/jrsLSVu37fb2fTtP2PK5s0EoxhrUVq9FfpNa59Vi4K43Fx7ozVK5ZvXfmtjwVvWVim3Dq23r6e1BGehdu6b7BalyiSk2wvrEs5OzVzDNClqf4gpjFGEkLi6vmS7XbPrNng/kEuglMysXTCfL2lnc46WC97/6ld5/snH6KlnEmMSdleWBC6XRKUcKMU4DjgzI/ggRnP9QO09Vk1QG1CmuRJuSozk7Bco70ZsWd5Cb6JF0fT9Tir1GKmcwxoRCuoQOT4+4vLN2Y3db1VVUAnEHGP4Q/L2f4XgrpSaA7qUspn++78H/G+A/wfwPwP+venff/sPe63gPT/84Q9xdUsqb6GPkiJh6Niu1igKscDFdi1wR0iSxU6bLCOHRIgB1Ti8H9lcrSRrL4KzWmflxu2bS4jrY6DQLK7ofSCj5KTOCpWhqVu8CqBEvBJDYCqwGbqBvhccbraYU81mKOOIRaAZYzQhDJNLX8/YObRKzOezG3WnEJrEPa6uHd57vB9ZLpfiJb/v0O2/OovFgHH2hh9/GzqY7s0NXGCtvfGPjvHtQN7bAft2MLsdnG+UuJN68kchi9uZ/42lwbRuwzf6VlC5yeb/JVlyyoaYFVZbSgDtM5dfnLF+dcX16ysO7pzirCP6cCMe2q/b/Or959izm25DS/vrss9ObwfWmx7EjwT3t+99wpnZ9yjMjw3uAp1P77HsqaDmpkm9n3O7R033VMXbQX7/PkKMKMDpKfOerqOCG1GROB3mm71y+/Pezp73L7Dvs3DrM769dpmSp8MF9aV9cRu221/v25De/v3vv+724W+dRan8peRCDgxLjG8rDTlv/sX9sf9cb/etcNFjKlir2fRbrq6uhBMuFAnxgMFQVw17yrSrKh4/e8oPf/B9Cgrvo8CyoycF8cOPseCjnwzBND4XIkKf3Gx2HOZCGkfZtwjf2ChF9P6md7Cn7vZdx2az4fhelPnLPpGmvSYHtFz7IUQaa6HIfmjqSjz+g2iBfPATm0quRQh/dJj7feBvTRfdAv9BKeXvKqX+CfAfKaX+l8CnwF/9w14ohMDl1SVVPcMHybTGQSazN85wdXUpg7GPTzhfX5F8QBdkfioTvgtS9xrFzBpmTUNsBnAOi5pobUbKNzPNTi2ZmBMhZ06Oj3lzdcXB0Ql37twhhARTcC+1BDKrNZVxWEQl9urFS5mWc7Dk8ePHBGVAWUJONz4u3vdQIt3mCrKnaZxAQoNswD3NL6VA34fJpdFgzH4wyBTApoePlClKvMa11mgzNeW0/tJDdzuI7QNdzgiOfyuze/tA3Q5k+waW+VIWtj80bj/Qtx/y27//0uupLzd592v/dTeVg2nEDytnWgvPf/B9/qt/8PcZr675rV//dR68/xTcjKpymKmZ9KPVxz5wAKzWK77zne9wdHSEUpoPP/warnLCRskSJBYL4Szv1+3Dan8N9tevlH1Q1V8KcvuGqtwnNfVB3l6jNOkNQojMZnPGcbzJfq21rFYrqqpiGIYbm4bbEFkuBVKiaaX6KUoTCihjv/Q+UhILiv1nuF0d3T74QRHSrf6K1ugiitz9+xW9gvrSIb3fU7etZiXYqklMpb90kNzeA186WG7tz30FtN8W+4QkxsDtg0rex61qg2mARdIYreh2gYvLK4bBA5q6bhDnRUkEZDC3VCixFB4+eYqylm4cqJSa+mhq4pLLYWyUiK20tTJSMgRKkkw8+RG73ysxCVtO6H1oBWOSOb1t22KduxEZKqVuqsZxjPhpBKKbsHyNEC/GcWRWV2JtQKYoJT1DpUQAoSetwE9Y/62Deynlh8Av/Ev+/AL4S/9NXstaERm1s6XQvqYmahxH2toxbDfklLhzcky33pCrgMoFq6dJKlO5mop0vGtnOT06QoVI9oHKOhazOSCSfcze8yLIBCGjefzwIT5lDo6OWCyX7MUgs9kSNRNsTeVCCYk0esHrlZSy84Mls4MFSTlSUejgKUqEN87UkDRlJ1OimqYR9kjZB0PJ5N4O9i63suo92+UWdmymoKvfbnaUuhVo3gbX20FWHia+9Ge3M/zb2VHOwg7al+H7tX/IYow3meZt/FX9yPu4tSm+lInt1/7g2AeMmArGOMq4JYUdu4tP2Z5/RGNq1m8Gst+hYoOuzJcy0h9tEO/fz2w2487du7x48YL333+fmBK7rpugIPlswzjIoTC91r4CuP1+94egHLrmVnCXoP7lQ0sSCWVuVzTSOFssFoyjF4HdBA399m/+pswmLYVvfvObWGsFopsO0JwzBkWlLA2GcapSSVngof17nHD4H63Wbh92b6+PQuW3VcwNrXZ/37RCT7YNOn+5ithXfnuYUu5BmdSm+9v95YP8dsDfM5jeVpsgECQ3AT5PwZUb4Ztce3nNcvN1KQZSFsbNdjuw3XRQ9FTRamgyKYrVr5tU28Y48S06OiKkxB98/yPM/5e6/wiSLs3SM7HnE1e4CA/1x69FalWZlaVbYtCNbmCmQY4BGJiNDVckjWazIfecHbezpRnNaDYLGjkbzpCrIYixAQZAN1pWV3XpSq1+LUJHuLjiU1ycez08IjOrQAyNln3TIv8IDw93v+p857znPe8bAvfu3Wd2fAJB2DCiCWSFoWMsUWt0XtK4yGxR0RrLaLyGUsJ2sZnF+baLt4nWCW9f+nELDg4OWNs6IGAISmjBTVNxfHKMMQKBGaWwCY72D5gdHxHahul0SozSVE0pomQ8Hec9riMjfNn2lZhQzYuCy5cvU5QjtM2hQxCDa8mN5vTogCK/wdpwTHNyiq8bogtkXVDIrOBmjXfUrWdtUDIsMhaZ8IoHRYnRihCijPkuTT6GOB/IxyMubV3i2eERV65cYbC2RuiSk2IwoDWeIivQMdHOK/AGhwyhZGWBzoTF4DpZgEjXCA2q64Z70asA7KCQBlmUcqzHcmMM4jFJOle2gpTQusPyllj3Kqaa0uduqH5bln4It3sVm/6irS+L++DeZ8MXA3m/fa7k53wA/1ygv/Beq58lz3Oc8wxKxaP77/Pw7t+wPamZHR0w2b7O4/sfcHv7N2RKM55BBKsVympAGQ5HvP7667z++uucnp6yv7/Pp59+Kvo4SvHtb3+7M5LIce35amU1G+6bvlmeCxSwEtzPFubueHD+OMjiIDlo7z8gGXZLDIGNjQ2qqloG9f6Yn1s0U6JMBtV6sihJQq5Mh8iojp+ul169Sx2XlV7C6iavfyaJ0MtG0/cs0tl+XISKLsIy/WLByrlc7a30r3HGBFLnFvWURJTPGMHaUxLZ6iw7qzz7rz7o91VSnxDUdUtde6wtGQwiwTnm8ylaW7LS0rYNQiawlAMRHpueHvPhRx8T25qd9XVmJ6ecHh2TQiJFj0Yw/ZQEwA3dRPa8avBJsTudsb6x0cFZgbIs8FHmFWT/PXme0x7u88mnn/B074BZ05JMRlA928wxX8wIQSafjUIsPOdzjg/2mR4f8ejxY5xrOvptEr/X0Huo/i2QH8izjFu3bqFNLrCMEmawaxoW01OsMVy9cpUhhidZSd0G2uCxKVFkxdIJJbMZpTaMhkN0SoS2QSXhEOssxygYFgUms6Ko2CZ89EzGayKqpMUk2GRWXHF8oPWObFgKD9YH0evuZvpDCrjOSLkNHrQov4WuZGyDo3Wt6N14j8lEGCmGusPj+puuz+Q7vLKj0tFR6JYBpxsSUUoodGmZeZtzN9vnYZa+3M3OsVrgjLLYP0dwUHMuSK7irf2N29/0FymUq9+fa3CegwXOLwDL4Jg84Lh37z0effpznr+9xsxucrKvuf7S8xycPGE+O2S9vPY5qOgi3q61JqQoInNJPHYvXbnMoqk5Pj7myuXLKCuKhPP5Qvoy6izwCJshnDMitpkhszl8YeZ+NsTUZ7PLc6KMiGuFxPe//312d/cA+O53vsOrr77KrVu3lsdtOp1SFELNW57HFNEu8vDBZ7QxoKxh68pl7CA/4+93GLxN5yu3/nVXj5VSGs0Z3bJny8TuGiBFFFId2o4Pf7FBCmdBXDb9pb2b1c9irSRu5yu8PnNXK4urcMAlUxeoUF6iY7Jo1VEIdWc8ryiLAQrDYjZDa4tvWzSKoijIdc7mxhaD0Ygsz/nk009xIXB5e5vnb93iYDCgtDLPAZG8g3az3FLXLfO6pnaBcrTG2vomh1WFUboz/whi3hM9zjt6faPl4qnU0lvXI0byKHCuYVHNKcuCXBthbEEH0TUEL0yapq0JbUsgCFMwxM8t2F+0fSWCe0Jh8lKaqZnokusEISkIisIUbI03mc1OcClQuZZ+SIVuxXedaH6e54wubZJllrSfCVddJYiePBOVSKLQvJoQcUmRDQY0MRISNDGAayVTthkqt+ioaFpH68VeyxQl2gNBE50MttQhok2iTY4QWkiI1ZkXkwAfA8VghC1HhPmMEFtS6jFdiNGiVI7WEIKXDEI6t4BotWjdQwIgrlPCNjBdU8p0vN2zm/vMfEPrs5viLK5KBtcHozPRrTM4As5DHRebqqv1tF4N7nTtBKUQgpiB5DFIdmKUoo0ROxixqGt0XqBiZHHyjAc//wFv7ZRMYsVP94+4vHEFmxTXhpaDn/1bNr/5B9idV2nclGxoqVPLYJixmFVyc+ic6DoKZiZDKbbTR3nj1deYTqesra3x+PFjHtx/gLUyNPPGG29QluVy3/sMuGflGG2X3OgQvJw/q0nK4FonA2dKE6PCZENSSswXFVprTk5PKAclL73xCvlkQDEo2bm6TVbNWCsTcxqevv8+R3/+M168chv7G2+xvzNh4krW90548t4PqEJgPFzn8UcPyG/cYeONl+H5K8wtZG2gjNDmHovGRE2rwVlL6WFUJ2L0nIwTbgBXT1u8UTircQpChOgTYEimoEUaoLmv0FokmZNSWCtJjTFWfIyJMhSmNCqDRE3mI4VXNFlGXWY0oWErJtZcYLZI6EFB0olGRYzqjlndolLXADUKpc73E86gMbusYFMCE0XczQdP8Aa0ofUKFpHgRJtoPBwxXt9iuL1GWVhUaNm99ymXxyW3drZ47sY1ttdG3M8zCqVE2dF7yrygLAs0hrwcYPIClWW88PLLPNnb49qNG/gEtXNCxkhCC9VdP08piPOp+AEnxUt3bos7U57hjCjFNotGjoGV/qFJCRXFi+DqpUuMy4LT40MIAVIiqI72iUA77z978KVx9SsR3CGJOA2A1gR670CBBmKQQaHj6QlV2+CCp7AyrqyMwmSikuecaJvnw4EEFSUwCSmhrGT33osgWYwiwas60aTWuaVG9LI5oiCmgG8heMnQ+6xDaxlgUkmCqIsR7R3etR2vVXwl26bCNTWxrwBSohfdlYaQONuUxYAUYV4vUCTWRsOzTBjodVL640I6a+T1j/dZ/irW28feM42ZM/aNJNRnLBhzYdoROBfML2bnol2uP3cTymufZeayz5E8ywkdnTTGwHiyxrSaMxoNqZyj9TN++e5f8MLtTa5vWPY/espwmLGxVXBv9xO+99rv8fHde3z07t/w8revUA4sVT1n++o2z/Z3KXK5yeumZlyuiQclkBlL0J7MWIzSbEzWUUpx9fIV5tMZ9+/fZ2tra9ks9t6fgxj6QK+0VAHB98qBInGgFMspYqUhxUhdtwwGA/7mb37EweEBSiteffVlnnvxBdbWx+RlCQkWtkBXcwYhcKcxrD+Y4n76A45mpwz/8e8TlSG4mr1Hn/CNV95CNTBLMBgVFOMBbVQMkkA13nSyE3TXik5gxFA+s5KNt4UmqEhMmqA1XiPJDzIIl/XZdAzoJPonso/yytqIzaSoO2h0ElqD9YqUCQSVJ4VqRJyt7mGjGHDzKcmJ9kylEvlkIo1qJ5pAtguMvUzCxWvp89WoNECt1YzHQ6w1tG1D1cxF/kGLFPjm9jYbO1cpC0uuFfPDE473nrE1WWNzssb21gZ5Ztnd28N6R7AGlSJ5ZinyApQVn+KiwKXEeDyknFry3DIZr9FGaFxAdddI8C0xODKjqU8KJuubWOe5vLFJORkRCss8RZLOCC4Sm4CPQrAwJPCB7UuX2NjYoDCGXCkJ+inhlCeqJFTxX5O9f0WCO50DvSMkRYhJuKUp0biG6eyUo5Mj9nafUVdzSAFrcjJrxGIrz9BRusoxJTKbMRiUlEXJdFGTOiGgJd1rBYeGiDbSCS9LESFbNn6UxhqNijL6XHSlr3aRqFUXPFLne6pwXsxEjNb41kPyRN9AcOQGhrkGV9G2jpgU2ojRgM2kz9Bz6PtJVbmIVyCNXsMaOpiza3ras2y9//yruGaf8SiVzt0wn8+IzpqSfRn+RQ25VWbMxa+LvwPQKXR8YSfKg1lB9I66WlDqSJjtkYeW937+p9zaSexsjJjOj3lw9AhbzCjYo4hHNItH3HjhMh/fP+LhB3/FS6+/zsZwSHNyxEBb6llNU7cM8pKE63oa5tz+rdL3hsMhb7zxBi+++CJaaw4ODvjJT36ynMJ888032dzcxBjDYrHoeiJnlUzf1O21YJxz7O3tEaOidbC1tcV3v/ddnj55QpFnXLp0SYaOUAy1pcEwZ8RGVqDqI6pKYzYuYULGznCDYbCYTjCucZ766RHHT0/YfP4Wl77zBu1kTO5g4DRHQJsbstYStcgvl0mTKQUW5kWkSFAuaooELpvQmoTTMvdhVSJTiSJ5bAhkwQGGaFaqNiQZQK1UbghGnyVN7RO18uTaYlMid4mBBYymahqePHnMRz/4Gd/83d9m56UXaBYVATAqE8VLazuI9TxMuJpgrOL5ffLSJxdFUQjzJLvBYDDg8PCQsiy5evUaOi8hOIzWHOztMz2dYrUh+H5mIOK8w1c1dV1DTMR8VTohkbyHvtdlDIu2ZlJeQiWNLoWVKvMBmuDFsD4fDMkHQ1o3pY2RcVFiBwWNd2AyMqtwOIILeB+k8rYwGI8wnQ3h9OSY5J34HiRxPUtaGtq/avtKBHdxAQdXO1zHA/bRUWYFTVvjg6d1LadHxyJkpIU61Gt/94wBpbU4E4VAUZZL2pvVdtlYXMVqZaRYhiBcNy1pdDf7mHrJ3SANjBgJnVqWbgP1bEq1mJOiF2XGIPodiYTRdAYbc6Jv0NFTmsTAgK9mpKTwQV6ryDPW1tYp8pymWdC2iSzTKwG1y/DpMmV1Pru21i71U+Rjp2WmeZEFIxn7GRa8yoJYpd9dZItcbIgum2wrv1Oqh3L6JpoAMyJXG/HOUZQjHj3Zp6pbbly7KgbOoWZ+8pR7H/6Cm9kxL+9cIi8K9o4DJ/vHvL6Ts9bsMV3Mmd99jytf/x1uXxry2Sc/Yr+ouf7iG5hincXCc3J0wmd373Pt2hVeeuEFfPQUyi73qacHppSWgRpE+sA5x8bGBpubm+zu7rKzsyN/YzSLxYKyLGldy2A4XLKFLg46tW3Lj3/8Y45PpgxG63z3u9/l1s2bvPzySxAjbV2ToRmVI3zTslZOOGk0iwQhMxxtDvnaP/1D2v1Dmp01fGYwlWOEoWzhvfc+YDTZ5q3XX2GRW1oNWZERfMREjcGgoiUYTcRQRAnIiyxRDUD7SPnoiCvDbR4ONCEmvBY9IKMiKjhUCFgNKokSZa3Ls8EzudE+35BPYHxn7jIwOBfIU2SIxgRR0Mwm6+S3b3L8N7/k6fsfcOvObTAGVQ5IUWG7ypGeAZbOc9rP+gXnezZKQdtNl/fJTFEU3LlzW+jJTgatotaopLHG8OzJE0LrKLIM1zTUdc2irmT6vKnFHSwEYT0pTeMCyYiyph2I5V85GpGXA8rxGHzqPH8dNs/IYk7rapL3lKMxk80tDhc1LipMVhJthkYRtEXcuDwBgXeiSeRGM1qbUFVzJoMhSWt0Lr4AhRaZgxACv3o+9SsS3GUhVBir5WREWZLniyl7+7v46Dg6OsC1NXlmyLQh63S781zoSoFEQYa2YiohBthyYkMIpK4TH0IALwwHlxQ2y8lsRtPWWGuwRssCkxIxBEJHRUsxiJZ0krLp9PiY6D1roxGDoqCX8GqqhbiiBwfB45sKFT1lbsgNNHWNC0DX4Nne2mJtNObk+Ii6XnSqiwo1poOF6Baai4H2LDD3UgRwxkBZbTT2FUvPRV79OjsHZ7zuL3p8tbF61ihlOWdwlr13n2652EBmFCoJLfdkOuMXv3yPu3c/4/KkYJim1PufMck9v/HcDmY2xbshP/rlA66UO2yHhjU3xbuWZ58ewfUFW+M1wrbhnR/+G54+ecJxm3NSa+qouXLtGjuXtolRzLOlvR0ELiGgjRLnpeSFWdLh6TbTlOWQN9/6GrPZHdbWxhwdHfOXf/nnjEYjvPd8/etvLyuToiho25anT5+S5zkHBwfcuHGD3/u93+Pw6IRysMbaZI0sM7i2pbCWpDWFNijvyZVhACgqatWQx5r5o2f8xV//S17+5lusv/UcezmMbc788JAdXzL57tfwmxOKK9v4oAhJMSsVzkQmIadoEnNE1dQmyLzAJ2SBynjm+3tM/+Qv2bj+CpOvv0FlFTrXuKKjJSqN0wmTGWLWwS+cTbEuL7ILm1KQK0tmEt44PvjFe4SPd/nuS99kcOUKLQoMrK1NeGFtwme7u9hFRWat0DczoS4mogx5mbN+TX/t9VXhasPWh0DTOJHmOEerTXif8D4uGThVVVNoiFrx5MEjiGLb6BtL09T4GMGIqF7MvJAwuvsoL3KiUmCNMJqyjNRAMprGORoX8Shq57BJmDKuaUgpEOc1LiYq56malqQ1tXP4LmkT5zklto2dLHOKGabImU1PMXkm7526ccekaKpGyB0r8wZftH0lgru0VBO51bRODLKda4h1zcnRIWujIfPZVCb0rASKFKQ8dE1LsOI644LHWsugLAkxsqgquRBiWlKsQDKVEAJo8XFMKTA9PeXqrZvCne1e2xjbZeORFD0xioyv8ZGmnhNcgx7kWAN1UxGUwmgIrSP6lrau8W2LdxVlPl7COHk55tLla2yub5JnGb5tOqPgRvRkci2u6kqR6IxIesrZyjHrf05dldHfDFmWXWAj9AvD+WGR1aET52T4pZ9G/SJ4ZTWwy8+duHBagZDo1DdTf3NGUvIUhcUDL7zwHMVgxDs/+wn/9kd/wa3NjDubhls3b+JncwiRae3ZP/BsmAmmgNQs2NywMLjMJ588o9IPuXq15LmbV7m/94yjOqdWI976zm/y0muvEvFEwtKBSMr6s2GaGOM5Hviy5O+aYOPxiJQS4/GIPM+5d+8eV69ePXcMYoxUVcXdu3d59uzZclbj5s2bFOUAbQpUx4E2RlPXlUg3o+X6cJ784ITRZ/d459GHcDpj8NP7PHcSuHrrZaJLQrONGe1pQ9lEnnvjdZqrWzTaoKJg3d6AVxp8wrgoEgJaRKVEahdiaLFDLVjtdMbP/sd/Tfb0M269/Sbjy5dYqCE+LwXOMRknKWLLISE4iq7/ENPZUBVKAqjurheTFCZYVGpxsxn79+6ydtjw7g9+wO2332b91TsituY95vSEvY8/4sMf/5g3/vAPCJlFDPGk0lMKgdNQn7v++uSsh8CqqiZGsZtbtZbsz3eRi9F603iC95AZQuPEazcBMUlsiJ1vgO5F1WzHWsmE3YaW/p0C552oWWY5g+EQtMFmhoSitJn0AYLvrPAswUdG4zXyosAFsVBUKSx7GMpoijJHx0jZViI2ppTENi/aN2gR3susJbVNN5BJ56P65dtXIrgrBSqK9dz8dIprRVGxOjnFuwZlDfV8SmyFZqRSEqlfpcSObwUPNJnFZjLG753vbu6zcfTQWXSJGJMMDs3ms66lK044VmtxO1EQQiL4FpcitWtxbYv1idC2+LZhPoPT4yOqJFOpTdNQzWd4V8uUXRB5T2MLsDlr4zWytcusb+6g6ezyIqAMeVF0+L5ARR3it3KkzvBy3emxp47rvBrM+8YgnM+qew77Rajl4vcXOesXpxL7LEocm1aDOysB9AwCS0rhYqINDVoVXL2yQ/nNt1nsPSS6Y3RWMmsCD1Xi8GTBO/d+wV++9ym3NtcJasILm3cI0fGjewf85MkzjHb81pu3yceXOJ237B6d8lt/8Ju89vqrBK2kyd2xh9o2nOsbnPUhZCHq2ULn948Ow8353ve+y/7+PpPJhONj4Ub3BuEvvvgiv/M7v8Pe3h7WWra3t+U9tOleWyahffAUw5LUtgQvbk8//v5f0/7wZ/z2vaewGXEqsTkNXLv6HMWVNY5SYN0Ztg4ChS/ZXx/z/i/e47nN36SdFEyVImoom0jhhaY7y7oFCi00wsyQVCBTith41ooxw2vXefLBfY5/8BfEMOe1v/8H5OU6U62ZezBZQYqJad1JB8d2yX4SD1OLj3It9hBciJF5rME7hm3L84M1vvu9t/nFj9/j8cO7jJ6/TKYtelbjDvcovWNxuI+OkappaJXtpCC6dEWdNVQvZu99P6lPaLTJsFlfoYoRiHD4hWDSNC1VVVO7Glphh9XzxVKWJHXGKVmRkw1KipgI2uCadjmYZYz4yyqjiaYjLXSQTV03RDSmGNAPk9d1I7IXCnJjGAxkoa/rCrFmNOgYSEGuuxA9i2qBcwGVIgHVMbI6R7ReidJYgnIoJVO57a9hQ34lgjsp0SzmTI+OOD05JYRIu6jZf/wE7RyVO6WaTrFKk1wvb6upXEvT1EQFySh8jCgrGGm1qKiqBa6uSRiSC3Jheo824tvZIip79rDg6PAIZS3jat5lJkjZiGSoGN3RrRw2ddOeKpFZzbAssUYawr6JZFYRvOD4Wisu71ziyo3ry0yQbJ0QFC4EtALn5CSSBLfV2rO21lPyWA4pxZQk+9PdtGQXRJQ6D8dcpCv2pWw/wi37JN+sBrWLHOWLmfrZ6UpnNxcITspqM5VlkaGUImm5OWRwK5ArxaWtDd7+xtv8/Id/wb/807/hxuV1VKl4eHTI/SenhJSz+2SP+4d73NgcUdULHh0vmJkSW7eon96nCvc5bhMvvfktvvaNNyHXRB8oilIqv3j2eb9ou8iIWd36BWyxWLC2tkZKiclkwseffMYHH3zAzs4Ozz//PNZatra2zgmjaWW6MTxJOsQ20nU1WGQwHPD6197gwbNdUmh44+t3ONo9oGmeULxxm4+YsYYjr2o0hmf+lLA5YnF8Sh40R0oxHxpsVGwspAc0K2Beaso6YTtHqlYFYgoU2pDNa8qF50RD3JlQtop8bQR5RtW2MMgZZBmhaclRjLWWAGXkGgM6QxbFasKRkrBpXA6lUeiDOfrJLsXadUoL2dYYpQNxWjH99C4PP3qf4fqEr7/xKhq57qPqpnqFri/3WzdItTpTsZqUGGMYDAbU8wqlOraXsh3jC0jivRtjIoaINZYyz6iOjnBVg+k0rPI8J8vF6EdbCajee1AsXc2atqUJjmwwIMtFrbKuatYDmFzjXCTQkDr7wHpRY7tJ8tA0HYSc5Jr0jqTFEMcoIwOQATKTQTEgOEdyLSop2qYl+ojVlrap8DHRVp25udZE/7cAlgnOs/foMaeLrixJiuQcyXt2NjdQbUu2vY1Kop62NhwxGAyk8ZEibQi4FKidW45Ghw6iEW0ZTZkVFHkuQdDKMBFZhi0LLl/Z4Xh2ysbGOuONdRlRDlEchpTBh4YmenBACtCtsK57/+n0mJlrmM+mnJ6ekJTIFq9vbHD1+jU2t7YoylJKMmtRupAC3RhicDSN4+TklPnslNwGhrnt8E0ZZOqHTHp6o2xnwdno88EdLgosnelhfxG9rH9OnxGtBvzVwaDVLCp2WfuSOKFAkZYLUuoGvZQClzrtH6PJtYy+xwhfe+MNrl+9zL/4H3L++q//illoBettFRkRpQsOGsWzhy0Rg7ZriCab4fGxIVrNledv8xu/9/tQZgQCUSd8DISAuBTh6TXA+wUphEhKGq2zrtH+eQEm3w2Q9I1p5xxraxO+853vcHh4yJUrV5YQ1qpwm2iRd70Zer30SFRGJI1DogmezcvblH/0+7j6kPpoit9r2bk55DNXs/ncG6y3cHrvHv/qw/e5+uoLxI9OufvwITt7z8gvPU9pC0wTsUm0ZpQBnZmOuQIqeRYqEVRk5CL5wYLpw8fEQcEb/7M/xGhFduUK08GQKhMHJ9PUrLWBvGl48unHbF/Zobp5HdsZf4BUIYm+Ga/QSqwXm+QxdUV1/xEHP3uHD3dnHGeWcHnIpJ3x+Ce/5OGf/4CrBExmaJsFYXaKHo1RaFBC3TTd1HYnEnmuquwr0hDk3JRlyTD1UtZWXge5dWJMOBeWmXvSnrU843D/AO+EUx+dxyihJy/qikVVYeua0LQoOh9YxJQl+VYovFpTVRVt42ibBnRGCAmF7tyYZEJWJyPGQ6GlLDLKotOk8g06LyA4IoHoEm0jdp1GW1DhbN8jnSyBJjpP2zHxetczf4HocHH7SgR3ay1royFoi4+Jpm5w80r0kn3k+PAQX9cdzgeEQNvUgOQQQQGmG2224gBjrMAzvmpk1Lv1LObzJQPCWIvXkJUDgoLDoyMcifW6xmS56DZEUfJr2op5U+FiN8zRtPjTBYvFHBoNTy2L6ADPxvoa167foBwOGU/WWVtfx/nAdLEgx5CswWIgKbxrmZ4cc7z/jNBWZFnGaFhidRBT6V7WJZ0FdzgLriGGzozivKbMRbx9lQK4is1ffO5q83V1oViFNc4WmdT12C5CGucvOGEzGUISSqT8FFFd1ra+fYXf+L3/iCenjg8/+hjtHWtZIHctpR2ycBlNVmDyjEw10ByTD0dUPicfGv7eH/0jXnzjNRa+oRiUpODxUcraHsPt918kanu4qr96zvcr+q03hoCEcy1ZluGcYzKZnJtiLYqCplMH7LH8GKWi6c6WlOtG4V2U4GUNrnEUO1c4jdvsv/vXpGeB4aVN8u+8DDd3SL98wul/928Zq5qv/eZ3yL6+jb+6RXt1QmEM2d6MgS2pDTQWkkqULpGMQjkxcHEWogbVRmb3n/Lgl+/z+t/5NvrqBsmscWQUi9xgB0Osj4wJ1A/u8elPf8yDn/+Umy8+T/lP/hO2t7eFHdQZjETOXwuRRK0dg+DIq4Z8uuDB8Xtc/bu/zfZbrxCGOa+98AKDjx4Sjx7iyoxHDx+gBiN21regzOU4xURAjGiUzs5dlz3O3j/Ws57Ksuhg164Ci4oYBGdfzGtOjk84PZ2SFQrVOSAVWUEdphitqStRdg0x0LiW1Km+eidexlmW0QbR0y/Mio+yczRVg9Y5ymZi5ek9PnTsPC8zNwMtmvsqdqqfMYjqpHNgs85QRqF1hnOtLBAh4RuBleuqJrrQqeRGcmNRpqM7678FmHtVLfjovfep6oYUpVnaLETqd/3yFU6PjomdVG9uMtqqITMWrYRb7mMAo3ExiMyvtsymM4539/CtI1OaZGWAJSSFigrtZRXWSuMXC3TriPMFtZKxfu/9somkU0QHj1yCQmmrFidE7UEZZrMTbFlw+8WXuH7zJtvbl2jahrppcE6c2qMLRB3QueD6xIirTqinu6R4wnhsUSHiG09Es1aOiV3wkSAlY+xLx58k5SYd1CLu80iHPgYxLlY9o6aHH3qcVHSre1/Pnh65dLbXht6x6aL2+/lgrmBpYHx+3Dz1+jlADuiUcCmIg1JQkCxtaZjHho2dCb/1ykvczAo21zbI949p7j/hxuXrhOGIB4sZt159iWp2jDs+RC8U+do2j+OUF59/CZ8UGZZskSh9DsrglHiTrurRr+7DWSZ/Xqp4tbJZXeQE1tK4tqHo1BOLQUFd1WS9jj5y8yYSyYhgVYoiTa19l5kZRVQiBVGlQMgLLn/rbQZ3XmSwPuJwoljsHVP/659SvrfLjRe2sE0k3L7F83d28MMCiyZXGhWkWYsBr6KYtTstNq9GmnJoaEwg2xoymx3y3j//l7z07W8w+zvfolQFRRPQrSO4OQ8//pDdP/5L5r/8iLwJ7Df32PjeMy6N1uRU204fJkll0saA0wllDBmwe/yMxY9/ws685fTGNpe+9w0KxgzrnGy8Dq+/xN6zz8ivXuelt75FvP0cByiClsaiTYk8ASEQcuTaj7HvNHXQpfgXpJBoWkdYTgzLR/RecHfnHHUzp26naOMZ6CFZUmysr5MZxdTVxNASQuTg6R66LKEKLE6mqNhZ7PkGk8nUq7U5i5lHacvhoyeczE9QOnElN6iU45sKFzqphOBp6wVWK5IJNJ2dZtKWxWyO1WNC0xLqFpsMoWpxXcBuqhltVRFdQwg1p9NDGr+gbuedWqRAVtZakvpbAMt451lM56iUyDsOu3Me1TqxwmtbXNN2U3gekxTe2k75UpyKbGdjZtEUStMoRWEtOGG5LBZtZyoglC6lZPy3qBtya5keHpG8x9UVLgTxPUTGOy0K1zadnkui7dgt87pBZzk3r17hjbfeYv3KVeh4sSEK57ttOuW8mGSBaj11WFDXC+rqCPCsjQvqxZRmsSDXOYN8jCIH2g6/Vl0a2GXjXaYidEi7YtLBuX8hLbPP84wWun9XaZVm+fMXcdtXmSZLKz9l0Mp0+hrSBKYrTeXPu4Wgc5xSHU1NR+kvNMETM02p4KYp2Ny5xs7VGzj/GH9ouDO5ztREYl3ztcuX2U9QN5oyRApdklTL2BhabSh1RtFETBuoVcBZMWHpgfeLVM5lU3glmF8M6KvsizNd+7M+Rds0KMVyETsblAKle2G3bpqyaxAmZUhKkaxlkCyDOpBvbpCvb7JIntPFAQ9+9g4vp0R5eZ3Z9TVOro5oSkOyGQlwKZJMJEtJFCNjwqtE0GCVIWj5fFlI3SmNZJfWuPbqc+z+4KccfPQxa996jctBMVADjmPL49k+v/izP2H484+5vtBUSXPpuZfYunULbQ1NaMkN5MaSGo81YpQTkaw2Lwa4vGQQIYuJa197BbcxYtTCycPHPHzyEZ/++Afcahzx4Igf/7f/T77xj/8xW2+8iQuuw8kh1wqNJi6v5TPcT46hXP8kRKLXRwmeQa5nsauzHR1SmtdZbigHA0ZFBtWUNrS0sUXhGQyHbE7WGKxvUNcNYPCuwUeHzrToQZUlg8EYknD2L29u4vJEUgFfz5nXh8wWjciAaNHmaeoKazRmYLERmvmCWdOy9nSDrKqYNg0Apc1RLnLqFqJ55VoInkzDcJAzKC0mjYiukgq+o0savby0v3T7SgT3GCOhbbHaQpLA6psG1zS0VU1sPdEHokqCqwYZv6XLKn2M6LbBxUDhB+zt7TGdzZhNp0KZhM6CrLvJl40/waF7u7eURMLAR/kMoWsWha7kEvZMwBY5xsLzV29w+84dLl+5ymg8IticuhXNmCwv0NoudbGzvMBocbGJGLLCMForSKHBu4rhYJ2qmMr4cUok0/uPCimsh1T6rQ9Uq/Ku/c+rDajzLBZWAj+fC2yqq1gEpjg/yNQfn1XhsPOwjjSZU+fpurr5lMAaVGahlfNm0RQBrLYMXYlzQ0xITELOtFbYaCmTeEkWjWLgDBMG5DRMCkvbetaspXSAk4GTmDwBR6sSjYKoNGbF2/OLGqurmftZpXOW6V98zip99OLx7bfPH/d0biFZ4vNRk3s6fF7MsPO1IW/97vfY1B/w9PSI6197jlgqQi49hhY5vklm5yhIqJDIFYQEp0ilWqDJk0EFSFhiZmF7wut/9Husj9fIakifPmLqoHj7NuWo5Pb167i//IjB3LHYHLL1zVfQ60Nm0eNyjYuOoCBTigzxLc0juAiV11wabpPfeZmPPrjP5cmIAXB88JR33vkRi8Uu1y+N2ZjkFJcv8+4773C6mLEWHVFlHX1VEgZrJIitEgD6oaJzAnHA8ckxde0YjSYoRKpElEMsWmcYnZPZHJ91mi51gtyQDwtssqTkqaoZdlAQfM3i6BDXVLjQoowMV2IE0wfDeLzOsCjIjUzVr+UZ43LI1lrsms+QW0v0TmzzVMCEyMZ4DZe1bK6NGWxuMQoOlGZockyEk3aBsQbXNvimJnrPsXlGriUZCK2DJJTslBKmm8b9VdtXIrirBDokdBKxnz4TNyjCylCTWlKlur/rMbmVIGSMmCYvFgtSp56mVzv83c0m2CHLZlnsA5eLnwvuBkVIiRBhOJ7wvd/8LXauXCEvCrKiIMtzGajwkeFYLxNtpcRBHujU4gwKyViDDzLQVDcU5TqZTuzHRzTVKd4tCErG53u3n+6jL/ezN1dexcb7bZUFshqYLzIOLjZX+8dkEbloSHF2jJfqfytmAf2isYSyV7aEQF1ivCy+kZmylNriosY6jaksea3JmkQeNVHnGAw6KkamoHCK3IPzoHxieniM2crJHChdoLSipSWaRMpTNzKpUSo/f61dqEpWg2//88VjtPoVY1hOp36R6uKy2bwyNbwa2FftEJ1OtCMjUtAoPAprgLrm07sfk52ecO+v/oZyrJn87t/DdWJbkUQy0lzLkgix5VEqlVMrsrAqIrx3IKTEdFHBaMDm7ZuUNuP0wSEf/s0PuXTzDhvFLapRwdVXn+dB/hfEYeLK3/kG/q1bpOhRRYbNNC6KYmvSkgTo3iQaSD4yOHHEhWNw8zJXnrvJg0/uoevEC2++jImXmf/iHXaf7nPw7Cl/9w//kPXbN7G5JVmD16LVFLTMpZx3YmWpYZTS2fFum4bFfM7R0SmnxzOGwzWGgzGpk972bSAFTQyKeSvaLck1HM1OOdzfRTUVvm0IvuVSvWBxfMz86JAYvNAxjZxvZTUpK4hJcbRYENqaVAjLqDo6QdlMJIG7ai23RujTTUOWG2LjuPvhh6Qorzfe2aKKMvWeYbAhMfMNNje0dYNrZbr13qefcrw+JjrH3rOnHUtQ4M6ss4j8VdtXI7gDGRLMRRNLY9CiDOk8KYhAfceTQuYNVgLUyk4KRSiIWa3qSrk+uMXYXZDyeEhpJRPrePAkXPDnYJmQwIWAyXLW1jd55Y238Ekwv0XryOmmxUxnfacl8NqOShVDAHU24q8VLJqa2iXKYsJ4MGI+O2FeRap5TQwV6wzR3d+YZXNQLf/Vxqxc8Gfc3xDCMpO/GJhWefIX+eyr2WV/A30R//3ignAWuL78QjPaiOJn7GRcDQQiXkecEiu3OtItYpGYKY79nHj0jKmrOXIVn9z7lNOTE3JlyIYT5joQrcGHhGsT2mZEnUs5r6UBJYMkn9e/ubjvq/tzxgT6Yu3y1QpmNbj3TdZec3xV3mF1ce1hLaUUxiN4uoVkhC2idGKwPiLcucbJvYdcuXOdUORsBEMbDcYmWgsBhQqKFBQ6RrkftCwOWknDPvSf02iq2YJf/vQXTJRlezjis5+/g10fcu1br3GQy9zIow8/wcWA3t7i1d/5Hk8nI4pWEww0CjG8iAIxKiWOQkEpgRFODnjwJ39K/PnPcJuWtmk5ur/L7dsvsddM+fiXP+c1W6CM5bXnX+Dl555DrW8wj0nYJXlB0mJVlzqpjXMVVTc4tdq8n8/n7D3bpW09xjgGxQiNeNa2jSP6hFEZRPFycM6xvbbOIC/QIYL3ZDGhW0daLKCqqBZzom+BhNGJRBDnNLWgDQmT5QzyjFE+EUct77DGknSfVCUyrXBAaY2IDWpNaQytb1E+oEPEpEjygRQ8MUCuwXSQX++1vFYU2CTxiE7ygigqs70cwa/avhrBXQk9TnUn1VjLIkahlHXZeOzcwlXygCbQMRJS6ihIchCcc0tMXXWUvNhf/CmJKzmQ9PkMTXVNyj4b7fm7qfsbo4WSONnaxidFEyLZYESuFXlmpIHTBnySTL8oB9Jpb1saH/BROOopRVwQVb68GDIerZPrjLpuKMoRTX3STaXGbqSKc3BKL3faO5/3rIH+M19kvlxks6wuBn3QWQ14X5bd9+epfyzG2FHYOjqm6dUq5bkxnjnmZNpggNqLBGtIQKaZ4/GZoc1aFqNIrKf46ZyYVSzWPIMNRXQGVSvWb64RR4GTw0Mee8ds5KlVTaMSIRl0skRErkHHiA2B6AJYzgXqVeORi3DJKh6/OjOw+vuLnqT9a8KqDd3517sIYfVbhmIcNHPd6cGniFeJk9mUx7MDxltjXnr7Labba7RPTxhur6GGlnlKuKjQ3Zoal7C0Ig8JqxRBJegmJ1X0rE822TJDfvE//Bkvbl3GP97ntd//u9iNEab2bJ/WfPajT1hz4F3g0x+9y8G9B1zZeYFrrzzPtHWocUYbPEZ1x04JUy3gWXz4C05+8NesHeyzGKzzy5/9nHJ4g+2dWxwePeK117/Li1nOzz79l8yPTvjlD37ErW99C7W1Tbm5RVQZwch8QEydmEdKpCScbq0ERl2FDIfDIXdu3yElhdE5ZTkmRcViIdow3jmCF2Nqoy2jcsjQaEoM1cmUIjgZnjxdEMoad7rApyAig8GBRibT267hay3RJWbHh+TWUB0fMx8MSSYTdo+xMtsQA75tUCScEge36vSE2ekMaw2LtsJphfMBHRImRGLv3BWFUaZSoJnPcAsx8WirRXc/OYRZ5H9d4v7VCe65sXKhhkhSvWlFN8hAx/iWM0zfFO2hD93BAYYkgwdKd4G9b2aJ47jg7gLxaHo+dlrCG1mWUbtWKE3eiwGCVojKRkKbgslkwvblyyibc3x6ysnJEVUlvomeDJtZbJ6TFSI7rC3YqNCmM/gIntxYjBlCED58bEUqWHdUTq0ztF7Jvnuv1W5bzQAvDuCsBqeLganf34sMkdUMs2/Irr7P6uv1z6c7N6TzC8DZTXn2mGsa8rJAJ0X0CpQlYEiFYhpmRH/ELx/+hPbhPYyqKW2Om1fsxjVCjJwu5py+u8d0NiWlRFkMOF7U1LOS0YdX+cbv/D6L1mM64+/gKsZ2Qpu1tCt9itWtr3JSOp9l9/vXQyerv+//bvXf1dfqG80hBuFtr0BZqwtG/3ibwd6mYWYieYBhHbEO1gbrqEvXefLXH/GTf/bH6JvXuBcKXv+tb3PlzZdxwaO1LKBoCEBQCa0SZUhoCy0JlymSMmgfWRuO+I03v8U7d/8Vz37yIVeuXKHc2eI0U2Rekz2eUR62DNY2aIuC4/t7HH70kPv2M96e/iZ3vv0m80VDWXZspOTxKRK6gZzi5ICxr7h+bZtfhjmbtuSb3/4tss2rvLpxiaw95Ud/8i8xKJ7ce0jcO+In777PzTe/znf+8O9jMSRjyEZDfNJE/fnztto3SikxGAyI2lJXLSlJhZySIs81WhdAS4y1QCHZkJHNcPMp86MTbNQi8aAtJEOKFshplSYo6XM1VUVpNXk3Ibo0DPGO2NMmUxLRQGU7zfUoUI9r0SoRdSL5INMq0dNWC4ZBbDyjazBJo7u+UPJO3kMhSWj0+ODIigI6SY+EkBN+XTMVviLBvc+0rbUELTsRU+rMKhTi2ZHEsKLn2K78XR/3+gC9ygfpN5VkcejnBrVS2F4o6ELmpo1Bxyi+lIBBsm2iZ3dvF21kqu745Jj5fE5hRRp4uL7JaDwhz3OMMSKBIIgMqqMtpZBE/D+3MmrsWo6P93jw8BOmx7ukWJFnCa0yWKE6KVgGGt3BU0qppY7Ml23n2SFheVS+iD3SP79n16y+bq9zfg6OiPEcJPZ5CKg3vdBd0FcoxCLPKkNyM6xx7B/fwzUP+MZbVzBaZExtZrHGYjuWSp7nZ2YIJtGGwP6pZ//gE8LsaxTDbepW7vzM5DSLBVFHVHY+W17NyFdpnqsVzsXs/Mywo7s245kTUc+i6Y9dSmlZBV5ceHvf2eVCoRVVFqmtwjpFpjNs1KASV15+lea1p1xen3D9W28xOq1Z29rsFClbTJGhu9eOJLyWDC9XWqpUBS4GPJIZFiS2L+/w1ne/y3vf/yEb33uT2fV1nhWJgbFMNiasff01Ll1aZ+vmDYajLf7iT79PERXj8bij3smofkxhSdINXsgOIcE0BU5KQz7e5tU33kJvbHBKZFBHPvzJL9ibnTKqa15/7XX2Fwtu71zmk4cPef+nP+Wt3/1dvFLUdYPKsi/UlpHjftZPCiHgnVT0wnGXiqxpalIKoDxKO7yvsWpCpkUo0IVA0gZlC+aupVQWXwzxhSOOhuR6gGprwlzLtKgSmWZJIuV71Xs5dJWGSn0VHUVGOAg8l0xEkzBaobusPkUP2nTwMlhtiERc8N3vFEpJ3e6j6NCojn2mUOcD26/YvhLBPSVpgOguS0dB6CccM0syChcDNir6/876o3JDxyTZ7VI0a+X1VRLNjb7brlbeWGstI7/0VnQW2zXNQhfkNIHcKNrgePr0MacnJxTjyVIuOC8yCqsZDIasTTYoS5l6U4sFVd3SurAMrN5HklOYUqN0pFqcsrd3n9PTJwRfkRkNURGdQtmuPNVpqb0hu3vWSL4Ip1yEE84H/lXfS74wI/0ipkf/+EUJVi68x+rzV4Na7WrAMm0SE1tQhpw0D+Q2cjx7wu67P+S3XtrmGzcucTw9wBHRnbuOCh7cApWUzDMojQkLhr6iKIakmeL+T/8tL37n91iEHIoBT57t8+juXTa2N7j98vPnqo7+3x4j702e+0x9dV8vHof+UK5m+Kv7vNp/MPqsJbi6gKzCNDYkRvNAnkPpDYMGaqU4HWtGX7vD5e1/iFKwuzZkfBpJg5yZSdjBAFc3ZNoIHVDJPeBVosJTKINBYZIh6ITOMyo353ScMfn2q9zaGZI9d5XDQuOznIVVZM9d5uo/+X1GhaVGMfOWa//gd3h+sIHeGDHNEm1m8SmiOp14HcEkSMqQ3X4Fd+cj7roZg52rtFnJbJjRaMXi3U958NMf8cZ/+A2sNXzw8ceU6xvsrI+p7t/l6HCf2ewUtbFBtJqoFTlnrKv+2MvxOzvuAtPqzvc0EZLDtTWz+ZS2XdC0C5p2LtTlNrI+eZHat8y9I5YFZbnOxmjMpWs3uHzzDrdGYzY2LLeu7vBv/vk/56/+9b9ikheE4NFRMvekkvTztDQ0UxJihvjcK0gapQxKh24xqLskzHTQpQR9azVKifSAr1uSqCGilGjjWC2uV00rXsuorrnb0Y1/nSIkfEWCu7AolJhI95lhJ+Prg2QLvSF0EvBZAhtdkFEKcX5XZFl+DotSPcOkW3l7rFoh6JUxRvxNu8AevODRWZZhuzVEN434Z+Ylymh2d3d5fnOb0WjIbHaMjo5sbUhCYYzFWpEvMCYjJQnoIksqXHxDToqauq45PT1kNj2gbaYYIrkdYHSGVTkh1TKFqs6CQh9MVoP0xeZg/9hFCOYilHAxY72IzV98vZ4i2HaTfupCEvFFkE9KCTLNh/c+4e6DA966/Tbl+DqlSgQ35ZOf/w35yQFbawXV/UOadgpFxqlrmGysE6oGExLjwYjHT58yWBtSZHOyNKNI6wSzzqPHH/DxTwtOsgkzDJ98dpdhWfLi6y98bgH7/IJ3fuFaXST7rc+4RT72/HFeVc88+/d85n4+81xpRqtEyBSNTZgoTVKHojGWefQMN9ZQKpEGGSYTn95AYmAMtsjQPqGVxipwCrxWLKzHKnFDygIEpfAWXGk5zTQLEnFyC+cSI6cpvcYXlsq0lFe3WMzmFCpHDweU4wkuaeY6UmUKl3XWjS1kUWESGAyZKXE3XuQb//Q/QzHn4PCAT57scvVlhzKKTRzPXxpzNH1ItfsEPSz5rb/3H/Bg74ArN67y3d/6LnY8xBUZFJZF62SqE+jFyVge3/P3gfeR+bzCdQ5fp6cnHJ/s41xFiA0hNrSuxoU9jFGsFZbf+YPfY2djnc3NLQZrE8gHqHLEZGubgZ6TE/noww/4wV/8OU1dM0iSsffzJSmCzTKUMcLKQYbJQ5SemlQ33fWRImghP/RJqKjMCutFmIACr8ZuViQlMf7uG7QiO5VEflz1tpyfv+cvbr82uCul/i/A/xzYTSm92T22Bfy3wHPAXeA/TSkdKblq/4/APwQWwP8qpfTjX/ceSSuqXBO68eHMWkJuqVKgUQlnNKnIyZNg82VekFmZUI0xEoHGiXLjaLJG0BqXIqbMSZ1GTG4zMpst/04pRWkL7rz8Eg8fP2JsNOtb6zjvcN7RNA3ey7izcSXaZuh8gCNjfnKIDg0ba0OePYkkLD51rvOZRVuLC61AOUp3o8Ni4quVQtmCxtdMp4ccH+9T1VOMjgxyi7GqK8e8tFSNUKxQmpggKdBWCzZvdcfF1V0pJ7o5uqNc6n7h6zbdr4OpZ4NIFtGBPvSWe5lOEjBcJKIINqOOEW0Nz3b3qU5nvHDrttja+RYTI1bRwUgJT0IlgwpaplEtPPfcCzzeO+ZPf/Cv+f7Mc9kk/OkTticVX39hh5GfokPO0FzmNIOnR4947oUXyJoA3tOmwKcP3ufl5yeM0VhadH3M9aQo2oYf/8k/41EacK8x/O5//E95/Vu/gVYJEz2Z1viYuonj3tRapnRThEAAoyiMRSWFTaIsGUjiyVnXDPMCG4W05byoipZlQYhxWVWFKAtgf5P31oZnC7LqFni5MYOGWiVCSjgTqUqND5HMecrMkGslDcFWpkFR8hlJCbQmGnlvYsIGIRhktiDTFmVC1xxMhCDqly4EzKDAxUgWDdZr6IZ85D+NzgAvjJuitEzrBl3kqBTIg5xi1WHbMXZNYqPRZY69fR104PKd59iYt7QZWBUxty9x8NmQ7//Fz9jJt/jDf/hHFC88z/UXAuspoicTQlkSjMa3LbbvDcGZsmiSfdU6AQFtEiG2LGZzpqf7nJwes5jPODjcY7GYLSesbWYYj9e4fvtVNrY2mYyG/NbVv8+gMyCvW4fOCmxRooxAXZlJbGzvoKzB5BYVAjoqOT6Ij7FKkUxpFJHMaJTyhCRObNrQoQsJ21VXWZ6RdCRFh4keHTW+S9JSkUkjtUuYhKefKGzGLEChc0qd06YO2dB0ye2vxmf+XTL3/yvwfwL+65XH/gvgX6eU/kul1H/R/fy/B/4IeLn7+g3g/9z9+yu3vCy5+cYromWexPPy5OgYXRasT9apvMc1LWNtKbKcQVGKCJgS+QFtDMlqbJ7zxltf4+j0lEVbY4qMpqoFi9cGazoHp46iqIYbXLpynd2TE7YvbzOajHGhxXtHXVVUixmubklOE6Lgim1b8cmH7/Lqa69g8pJBWdK0QRgbRi8VKyNKVOKU7rxSZSxdaUUT57S+ZladMJufEHwrTCGt6G3/lAG6CVRjRBK1Uw7oqpVuOpCA1pZAZ6ybrLwPXfOZM2y8D+ySfaplFiqQcc9vhxi82B0mxaz1LDzMjWJvd59fvPs+I1swuHaLItSsZRkjq4nRoZID3QdRi0oavEGnxDgv+M3v/gYPb97nh3/8bzhtFvzuW3d4YafEmBnZeAfqjGqm+cnDT/n+x8foSzUvTHao2poPntzlL5/MOM5nvPn8BkMdGVhDGQsGayVff+sy9rDh5efe5NarrxBVhvcNVjuikUAeklSJOkGic9eKkaC80GN1hkkKlTSNjwQDi6ahzEsaJ1O3lQudNLOnDt3kbUpE78X+jJ6hlZayD/2iKbnPGV9ba0MeFCZEINCqSDKJPCa06xy4FJiQGGkR71Kha76RCAqBCUgQIiZJ6DEZRKOEUaY1uRb/ggwrzVOV41Si0QFlFLkODIwI7kWrJSqkBJ2CYWg9pZEg1S9akj30d3AkV4nWKGmGGo3ZzNGtR7mWdHmD5/7eH5DdfYnLt24wurTNMyJpmBGtptYdZJGEo18o1S2Y52FEpRBfWBWFvZY8h4dP2d97xtHxAbPZMVUl+lEky3CwwZ3bz3H58nWy4ZjhaLTsj1AUJCXCZ0VRdItIwqQMayOHh4fdwhzwoZX7pYPbolKkIM3VGMQUJuKwmSIEGZiMuuu9eLHvw2iCSmRKfFJNShJTSJIMhLBEFYgyn6CVFTpnMmQ6A51YhJqlIMP/VLZMSulPlVLPXXj4HwG/133/fwP+BAnu/wj4r5PU5d9XSm0opa6llJ78qvcwxjAqh4LhdhlIcA5rjGQj2ohbSxJWjA9hKQJkrWTKymrKwQCbZUueN5x5fvrOsIOO2aC1psgdx0cHLGYzJhtrVPM5ddt0GF1D29RUszmLxVT8UZUmYXi6e8BLr77O62++zdpoxMnJE4bD4VIESOiWZ9xnpcQXNkZPUoo2eRpXUy3mVItKtJqVxnY6GcbI6yxxPLVKo1vh/3Les3TJ56cXUTpreKYO2rqIyV+kBGqtUWaIS4rGKO4dP+XdR494Ui3YqxfM24arV65QPbyLOj7i7edv8/KVK2JhloxMiaYIKso+WMiTRjtNTsHl7Zu88fXv8sP/8V/wJ7/4iAfX1nnja7fRPjGrHH/2s3f5kw8/4Lie8fTPv8/Lm1fxKfDzu5/iU+Tpg6fcdy03L43JtVDnmuTYd3s8nDW8Qsu29QxSRZYpnBLd89Bz93UQeYSkSATQHmukIR6DJ6UMrKY1hoPFgp++80tuX7/BnWs3MCHx2eMnKA0vvfg8OsFifopVislwJGwo3zl2qTMYqGfRrDZeu7vrXE+jP0+rDJ2e/roKdfXNxF4MbXkNcIb791/9RPEqRCQLy1nfYLXPssrL73/u/67/jKuQlTSQ6Yytz5rxEZHQzXLR1B+sj3n9m2/jdaKNkWQ12aBg4Vp8kCai6QgM/X6sXps9DXW1X2SM4ejwhL3dA6p6TlU1ZHnBxsYGt27eYWNjm7XxOlpbKIqloUdPvOjVJfs+XYyRQhtC61jMKumPxYQ1IjuhtCEawdq9NqQso06RsjMLQhs8nhQRjXetSDaDJH7L0ViitkRjUNrgQyQmJewcIx4ECoT7rsAOBui8IBpLMhnJJFT0aM6c137V9u+LuV9ZCdhPgSvd9zeAByvPe9g99iuDe13XfPLhR4KhJ8k0nz5+Qj1fcFoUPHvyFIXwxw1iXC3URiVZojFgNaP1CVHDoq54+PAhdV13U6qSZOiu6w1yURbHc5qmZv/ZM2L0ZGVBSJG2rcXhPoosaNXUJBI+JNCWum75/ve/z8uvvI61opRXL+Z458RcREdhxWjNoBxQlQNC24pHrG9xCprFnMVsKuqWPmAz1ZWdXfDt//scdr7SPO0yQ7omKz1Hv5voBc5lPl+2XWR2uGRpsDRGc6oy7k8XPJie0hSGOBry3ukx7y+mlKfHqBzW1yfkgwJXewyBpBJJRZIGZSODlEOTKMqcRQq88NKbZKrg53/15/w3f/zXbPz453gadLDszR3PbMLpNT47cEwP96hdS50P8CnDV4r9904odIW1Cp9aWhNock0qCnYaR2ktm9ZQeYcrBRtN+C4IybFRgNYCddjOAKZxQn91IfHg8IBf3L3L3ae77IaEvXSV9957l08ePuTSpW3CzmVODg/46Je/4MVbt/jem29RAiYqUoSysEvlyNXAvBpIezjsIv/9PH5/do4ufr8a+Pqf+z7Uuce+oPF+ptly/vyvBlTRWP/y4a9z1+ZyyLAPvKJe6EJEFxaV5/guqDd1TSSCStg8J/qwwsCShUGZ872Siz0R00kCzBcV80WNNpabN5/j+eef48qVK2htmM0XOJ8ocr0M7KtG6f3rWiumNwpNqCseffYpf/2X32d9soFuCwZWhiqzLAdlaX2g2NhA+SFBKfLJOm0MMpXsvAxbuEAEMquFx54ZyHLqFFFFSVYOiD4SlJHjEqPAxsagOti0mk2JWQ55gR4MBUpOGkVaMvxg/0vv6//JDdWUUlJK/TusI+c3pdR/DvznAIMiY393Vw6+0qQQOT06kqAVIsF5rDHCq010JYsEd92p73kXUZnl+PiYpm2XLAjnRKtbpbQM7v1FE7yjXixEntN7UiNNC1JCRSVYZtcgbTu50dZ5QvA8uHefd995h+dffJHRQMyT59Mpw+GIrCiJSYsXaicvbKylRYmFYHBMT4+Yz05JwXf+ohqrO7MBYzoe8yorRn8uAGQ2Q5neEkImQeWi7QbAzp+nL8wK4azRt5T81WKw8eRwn8+ePmLW1mR5xmgyIZsMeXy4x8I1FKXl6ckxB9NTrg+uYqIsvFKhdGPyClKXxeRWkUdD0hl3XnyFwuQ0jeedn/6IMrestYardsygzKhCZKsoGcwjw8mIw7gQ7ZXGcfX2TaZzz/z0iDJXkCcq0/Li66/xH37z7zKqQFdz8jLDJREpU0lGwwwyqSwtgojpMjaXIsoWqHzILMBRUkzznKetx0bFo6rm2FjSzg67JP7yo4+xCh7MK44/+ZTNS5d57cZNQBqaX8Qyuhi0leJckFmdKTjPYjo7T6tsm9WfgTNBt+73/cLSB8SLDd7Va6N/7qo2kV7OhvC5v1n9WxIis6GVsDoENCYBxaAkOLm/iqKgjo5UyP3gU8L5FmvyDkpRHWuE5X707/FFDDCtFcPhiDff+jrb21usTyYMhgNSSuzu7rFYLDDakOcFRVEsg3t/7Vvb+yqLrHOMCb+7z7/4f//3jPKC8bUb4CpGZU7yUeZX8pIQYef6dbQ1eA2XLl+h9o6kerpswrVOsn7ksDR1hckz2qbm+VdeZri2RkiiENr6gEZsATObQRLZ4/n0lGw44urODlt7u0ynU2K7gBiWFTr3737heYF//+D+rIdblFLXgN3u8UfArZXn3ewe+6KL4r8C/iuAjfEwpZTwrUMZ2w0jJAn03aRqICwzk4RYWFhjhGerxFEpkWi9Yzaf0bYtbetInfK9UQqBwM8CmzUGknBQjVLkeU7diLob2nTvDda0okceISTR1KgWC37y4x9x5epVhmVJvVgwPTkSvW/bDekk3Y2aa5SR14xJ0dZzqtkJrqlkJD+lJRMjJZblaZ/VCbRy9v2Sn63PT0Bq1UMu9JzJ/ljLTajOdGFWWSGrgSilRAwNJ/NTPvr4He4/fIg1GaOQKKeKoU5Uszljm4iupfGR08Nj0vo2JkhllRRiIdb5RKIcqdC0foZKAZKlKCy3btzkH/zdf8ClWDJWhvzZlDtXbxKubnBYLbhsxvhnJ7z0+ivcP92jdhV60fCt3/o7fPDwMft377FpxUhhd3bAd97+OxR+SDYck7TGqYSNIlFLEB15o/sg2FmxpYQqMxqklD6ZzvjZ3Qe88+Qpx0TasmR3seDTJ0/QZcHTvT1OTk8xu/tsTNbwxtI2jt35gjt0UFkQmd8vC6Znj6/IN1+YFu6DvZwXWA3wq+dq9TX77WIgv7h4G2MFGz73HmevfxEq6q+hvrm5TAI66ETuIdMx2aSvlLreUNU2qCT0vjp5gtViXK1E59xYK5VmlKC+GtB76Kn/+eLn0Vrz4ssvUhYDhsMhRVFQVRVPnz3j6OiYarEgL3KuXrtOkedLk4t+3/rBM++7yXbgR3/+Z+w+fMh4MEAFB8pQt67Tu1IkY7E2xwNrozWsSqxtbjLS0gwPXeLgXcA7j04RoxVNtWB9a5vFYsp4Y4PJxiZRyQIXEkQnE6dGq2VMUMawvrXNYG1Csag4ns46WY1fb44N//7B/f8F/C+B/7L7979befx/p5T6b5BG6smvw9sBUIJHxhCxeUbyQhvyMXa+hNIi1B0U0xMzOpACbQw2s0u3o9ULIctkzF6v8MP7ixSdMFZRDAryIkcbw3A0Ei0U55eDCbrQTIzomFRNjds/ICbF4f4en33yCdevXye3lsV8xnw+JS9KMBlhGdy7khaFSFrUBFdD9IKvEWnqluA9o/EAm1vQSfi7q5BMOlO3hLNyWps+4HeBS8sE7kVI5otwzIs3jFIKi2eYBQrj2Bhobt+8yfHTQ25sXWZ/f5dXb9zk7sET7MY6t6/d4I0rN8m8TOL2N2jSQJT+Scw8yUgTSqWAQdg4eZGztnmJ0/EldtSAw7sfcCsfwGANu2hZqz2+8QwXDWuNJ84XlD5imym+3scujrmyvkOhc1Rr2AwZ5COOtKIqBCIYRoVWUolppbqZB9VNdVraGJktWvZ9S1I57376kJ/dfcAsy2iMIkRPaTVtNeN0OqepF2S5ZX1rG2MtdVNjtWKwtUmwBhcDudZLPjac58OfF3HjXMbeB+JVbZtV7Lt3frq4rZ7PVYx/NbCvZr+rP/ff9wG7v17l51Vm1Rk04r0/dz3RQYShf68g4nvC9JIJ1uVztRWYLHW2iwkZBlJKpCN0N5kezv7monzDOdtIrbG5xQXP0e4Jz57tcnp6inOO4XDA5StXmGyskRd514tK3SBYOPeVZRlVteCv/uyPefDpp6gkGuwk37VSRQXTGIu2hvzuPXauXKWNnmf7+xTDQacjYyCJg1JwQmO2CqrFnLv37lFXMwbDIWvTGQFN6z3KZDSLiqZpZKo1BNHV8p6H9++xsT7h9OSEx48egxPtm9WF/cu2fxcq5P8daZ5eUko9BP4PSFD/fyil/jfAPeA/7Z7+3yM0yI8RKuT/+td+AoQ1MFobk2KiyASDG4xGpBAo86K7gQym47hqpbBKmiJ5URAVeCLrW5tM1tcJ3nN8fCwXaZfxDItSDH9ttiw9r9y8ic1zxrNTbty+DdqAFi5v8tLkTCFRhzPXp9PZjJhgOp2hUTx+8IAyz1mbTGiDYz6bkhdDytFYgnuMhBSloZoSPnbjyb4mBicXMREXAykajB5Lw8x/3vpNad15RArNqg/4faCQhL0b0dZnQ06rgeI8fv/5mxxAuYbtccG333yNm9MbrOUj9h3o6Qw/m/E7v/1dvvnKbVw5ILrIurHYAFlR0rQVyWqUSdgYMQmiTjJOHrsLLgVCGyAYTPAM2siWNTRRkaKjmp/Q1DOGlCzmJ9y79ylVGXGhJVUtn919H6zn8pU1WDTM5w2zasbB/JSMbaZFyXEeWcMyaiPLQkhBVIpoM7yx1MnQJMVhNeedx0958HiP48oz05p5jJgiY5IPmBjF9MkD7tx5nqg1nzx8gB8MyNfXefnVVylS5MN7d7n//ntcGw34na+/DZwF1YvH/CxISea+ugisZu4XK6r++7P75ryJNOqsCuib+f1nWMXmZaBOfy5A2JXMNnY87N71+eL1Yq0957nr+t6CUuKkFGXgiBDRqYcXzyarY4hELwlUQkuTURlChyd/UZfoIu6fEoTQcnLqOD465ejoGOc8ZVkyGJRcvrzD5cuXlsmPD0FYUyEs3bN850eqlOLDDz/k5GAXV08hRrxrpR8DZ/eXMRhvaFVGdXrC6XwuMzR5xqKqsVlGiom2aVERsiJDqURbVzx++BDX1gwHAwb7B7Qh0XgxAAkdNJxZsUq0WhNcy+6zp7jFnKqqWJyeoBHNmi+Dyc6dz1/3hJTS/+JLfvUHX/DcBPxvf+27XtgGwwFf/8Y3ZAU1Vsw5vEMl2JhMOgZKIrdZp/joxZKuHGDyTDKHzHLnxee5cu0ajx8+JMbIbDbrrPKECmm0JrPZMhvY2tkSGdPcsH35EhEtJtsofOtJIRGcJ/MR7x1+aVLRdb9D5Mnjx5AiL774IuXGJYJrqRYzlM2w+WCJjUnmromA961kBTFIkI7SQCrLUrIzxBFdKdMfWHrWhDoHvXAhMJ81XQW2+TxOf/FrNaPrg8JAG1QbGJqMq1vbFMmyefMmP/2Tv2CcWwqtGJY5lWvJkiWL8nmnEUKmSJ3lYRYjppPFUTFiYpQbWCmCTvgIqZOOzTGoEDk+OsSsF3zjG28z9obPmkS0iisvXOPpwTPiyZSvvfQqCx3Z/fAe9z7+CGssdW6oMrCFxlqFjdJzCWiUSp3VncYbRYXm2XzBx4/3OKpbKq2ZRqjKEbO2wmcGazXPP3eDF69s8+rWOu3jh9y/e59R47k+GlLNT9mdnXC0t4sJnqJt2VSJ08xw5/IlXr967XPHvA+GZ9l0P3fweWeozwf081Ox8Hlt+f7fPM+XFYK1VryGV6o2maw+/1lWr6OLFQOct1rs90N3FMsllKPO+j4ZoDnD7vsM30ehCAp7LGE6aEolUFYox+lLAtdqZZJSwnvHrDrl5GTK6fFcJLkHI9YmEy7tbLO5uU5RGnxsCS4uK45+f9pWdKSGwyGLxYKf/fRn1IsTgquEVee98NoT3fS8QgdP0ppERqobUtOgmgYDZF38Sj2+CkJPDp7oWlSnSaOCFx2ZKFr8KXmZ+FVAp2vlYyS3hhwgBHQM6CTuUEp3fbP/H/Dc//+zGd0NeVhSkuZFCkEYLLA8YKmbWvXdSjcaj8k6LZeyLMk7E+z+4gxRTqqyGVHrpQwswMn0lDZ4XIzsHx6K0bbzpARt7QgdbhZ8g+uCe+taTk9Pqaoa7yLz+Yy6qlDArZczsmJAVrRkTYvSWYePycRaSpEYEjE4YpSBB/o+QkqUZQkIbquN+Zw40Cr+mEhLZkJ/klczGqU+f+q/KItc3ZYNugBGaYrc0KRISorBcEw5GNL6hplvmSeNITGyhmp6Sl4UHMym5OtjVGY6wwj5DDZqVDCSzQFJJarY4FPXJ9EacjFVyJOhmVW885NfMHaaxeN9tq9d5sHHd5k1c0qf+OAHv8AHzfHuPuNynXJzwqw+YWNnhzLLUa0jSx2H2GbiFKUgWbG8fXx6wk8/ucc7D54xT4YGaBXk+RBnDLUXJsd8PkX5MbPdx3z2w79ic7LFt156jWlKvP/4Cc/mC07rikhie33M63ducXU0ZPvyzucmilfPT38uL8Iw/XMu/q3gw+d9bfvXOIe1w5IGu/oFLBeXL6rUVr9f1dcx2vQxaol/X+wPxH6GQp8tLqq7rnUSGMRohe3gFu/Fi1WhSSphbSZURa1Ba5E30FpEuL7g+ly+R7c/WW4pipz1TYPCMlnb4PLlHSbrY0TPSSZCrZVEyTl3bl/EnCYxn895+vQpeaYxnUqnUcLtt1bkxw2iM2+00LNLrakBEyM6BAqtu8a96uiTUSpFrTGZZVDkpNCCl+CulSE3SuSbo4gTGqPJlBX58xTIjUwgByXaNDIom6DvZ/2K7SsR3F3T8uDDT5ZZR/Ce44NDmqahqioOj46kyeokICYv3eSeEpllGcWgRAN7T3fZ3d3jycMntM5Jpz6IzoO1Vho33V1QEVm0NT5E9g+OMN0Ua0KJo3kSKKR1tZR0QeCS4AIqSlaflwXVYsHHn3yCU4oiLxiPx/i2JilNiFIGh+iIyZOS6+5AGVpKRhpwNs8pirzjyCvw/dhEWvnv7MTS/U4DdOyYpDWKfoovigSp7m8KEWKLSlgjKkZAoawMpCjETtD4hLeKeWjwVWJoBSZrfWJRlsxSxrtHUxRrfPzeT/jGyy8zLCYsZnN++e77vPjyC1y7ukVuFJKsycWt2oRqkKEsm6N0ThY8Os1pF1PqwYCTWBPmGflkxHCQiavSuiJdylmfTPAnivp0ylRVVPMFtarJRgNOVcNUtVTJYYIjtI61wYBF9HgjTfCYIlbl5MYwKYas5yUTBTp4dJbRKE3lxBVIaY3Rlt1nx/zVswOujwrmi8ggLlAffMp3vvFN/uC7L/Dxxx9z/8l9FvWc77z6Al+7c4eJsQwjdHhZRw6QarCHLVAdLVeJMFT/XLqpxhQDsYvhS5hNXqFLCLpMO8VlwqNU/56CiRutuxH3Ffx+BSP3yQtkGROaTivGWNokEgfBiCmH6QgJSnesrL7npYQZpbqBJpcCWhtp6gMyBQwoIxVr7P6+7xNpYTGZ1UrUCYynuqb8mUWk0DLp8P+eKZdlwq4zSpPpRDEouXxlm431CcZqlJHXaNsGH1LXI3FUte8GGkuCi9gEB08eY2PLeGOD+XwmHs1dRWGUJjeWLDMypKgVSg/YunaFeFww3NqQAC5nQITMXFfRGBmOS05jRgOir6lSIM8zRIPGgJd7Fa9R1nT3dILgsWtDbJ6jrMYcW4ZqiDZn/UeeHXxpXP1KBPfgPCdPduVCNCKwOz89pWoa6rpiUS1oWyf61SlioyipCfNBZsI1CrdoqDnlaP+IZtGeZUXozmkJghfzAqUUi0VF41oym2M8qBSJ0cvAEppIIrNWJtLoxvs7IxGjjGCDShG1om4bHt79hNg2jMdDTJZLtm4zNBGtIsYgX0pB6qZHVSTpwHCtxGSK2Co0BpMsUXXZfRfaA5FMIfxxJcGfGOiVjCVj6HjbGoEjOogHIBpNtPLeZzh0N+GYusUsBOY6kHKFDQrtI8rknNrAg3bBp0dHUFdkkwkH+495XM8oKKGOzPaekdKc7fJ1is1NTMyAkplpqKoTLqc1cpXTeNDJUrQNnB7j5k/45eIzjtQJ43aCevIMpx2Napi3cz5u3kMbTdPUaKUorCY1LXhF4UYsoqLKS95S36VZGIbrm5x4Rz4qQXlUVOTKgo+k1rNtcn77xZd4fmOLhQsc2Yx/+9k9jo6nxCSKk9ev3cL5wINH95mlQBjtEI0mNC3ug0/5J9/d4fdef4uHo5L3PnmXHQJroeVSUZAWLdmw6DTzA1Zf0I5PEJVkpsKMOo8jCyusg2bodESkobICw0gV2r/uEgbQRjBvWBrGCDwlf9+beqToJRFQYhJhkiL5INVeN03poqOI3XshPZPeIKLXkl/KbWuRYDgbOOokqyNd5d1VK1r0VZTu/ASiJ1NnrlW5MhCRYNlJNehlf6K7ZJUMBCpSp7Kq2JhMpOe2NkIb8N5xfDjFO89oNMZmCp1bQtLUtXiQTkYj1scZwyxRHe3z1kvPsasbWhcobEZupPeWQqAoS4qyFHkPrSlHE55/6SUuzU7Z3LkMuYiqxSSua8FHNJo2thijCW3LaHudw4M9rly9wvXrN/BRoWxG48TovPUNeS6wMVEMip49fIRNiSwp8kFBbJvlvZtSgk/ufmlc/UoE95gibafE6F1DSomqqvBd+RTcCoSRzrjDSsvIMkaDla+Fb6nrihBaAEIQmMUYTQoCjaQofodZnuOCx1gjfNwYpTFEJznb49JBAqgx4l5vrXg7ZpmlGBRoL1OZTV1x994nDNc3ePObOeP1S+IipUzHO+8w866JFpNMGhprKcsSrRVxKe+5TPKW8Ipa+aG/qaPqNSY+j7/1ZWxvC4cSudZAQilhGUXVacwkyfajVhTRolPOPDkaq2gI3D94ypGbYwaGU3dKdXRKGTWLR0fUvkbVhwyZs/vhj7mnHrDxxm+Qj17AmjGHi8/465/9K75+6UW+/vLXUcZgoqNaPOWTd/+YjSsRO7BceeESucpIyVNkJZmdUNqrpLbFRJiUA5ILBO3xOqCUIZIxDYq7x3NO9j7g5uVv0fg5yY6ompYsNwSfcL6hLApyk+FDpNAZt65cJ6CZAkfTOZ8tGmaNZ23nMpPJiGdHRwxyTe0qYqdxFEvDg+kuP/zoF/zBN77BteduQh5EF8iJLjvqDL7oz9UqHNInHau01F8Fl33RppRaTlmuwiOseAX3xIGe5reEdFLCIjIZaBk2izotF5UMMZ4OMYJRoIwMzmjVBXDRODGaZQ/IJNVBpUHYazbr1pte7qLLSGSAQ2SKU8fq0ZkELG26wT2w5ottHlePZT9pOh5pNje3mGxskFDs7+9zfHLKcDTi0vYlEfHTSnpo3osYHyKBnRcZIdQcHh4wHo1FhgLAB2IEVzdnTBmtMSmDzKIliqOjmIaXg6HQbzu8PPqIc56BztFa9Jmyzjp0e7LBpY0tXABlLM5Lded9sYRaExLcc2NwVU2ZCchprZV9SWesqC/bvhLBPQHRdvQ51w2ZGEPRdeRJnadiZMkAAeEoJ0TH2mtImaFx7Tkz3b6E9d6TlGTiII0fY+ViMtYQ6DRDvCcmGb4wRsaSsyTTjVYbovPMp1MgMVwbs7m9JbZ8KdIsTgHF40f3mGxs8tKrJVk5RpnUTdcadOot8wSuSSlSlsMlZdMY0+F1QeASpZYMg35R6FkxMUaE93DWaP2cLC9nGKVO4lObVCJosKZLv+jmBAygNLo14BMxz6gzzacHz/jRvY9Rl7d4+do15t7x4d3PsHtHXDJw+9qESyPYGlgmZcl89oj9R3/G6E4BNmc+e8b6OGIGC2b2gEWmqds59z7+c7L4gNdubeDTgmykwE/xrWhslzojR+N9RZEZSjOnaSoivQyqsF0CmltbQz78+AfcffqAl779+xSb13m6e0hwjls3r7M5meDaprsxDJkxJCxJGUqj+AdvvMYnoxEfP3iEM5E8NuiBYV4ojpuAV6ALw8Ik2uB4fPyMk+aUq6Mht29cZ2gMpnK0p3PKvFhS7FZhkazLos8C1nm3rP9vgnt/c6/KD/TY9yqL5SJOLZWDiGClnj2kEhhRJOwDVtExx6rkuwpQGqRaG5njiFJJyoKgMD3lt5Ok9Z1ncPex6KEnpeISYuyMLNHKC8VQq250X8zDE2d9pv5Y9gtif20XRcHW1hrDwZCqqTk5maKN4cb16+Rl2dGgu9fwjhB8R6yw0vxVmrpuePz4CZmKPPj0M/af7oq+SwKC4PLWWow10hs0htyUHB0cMqsrdh4/YbSxLnTI2BEo0LKQKHGBi86x++gxB/t7jLOC2HjqVnTlnffgAyF4fHTdIp1wbcvh3h7VdMqwHPDk8WNcU0nF0vULftX2lQjuAjEHUTvLDckJnGCznNxacisnHt8pPHYmw0abpSKiHRSsbU3QVUWWW7JMdi0luRjWxiPJyLsFYzgccufVl3n05DGXr1xlNF5jNBoTYsI5R9YxV1ISO7Hef7WtalRMnJycsL6xwaWrl2mdwwVPvRgIxzbAw3ufkhcFt+68RJYPxbBYyUWllniinMfBoEApRV1XZLqjmKHPMviV49Rvid7qrmNwdhd9/+/FoZiUEnkC3akdCgbbjat3JX9IimQ1PjeEOhAcPN3b5UcfvctHJ4dkm+sc7O8zxjJqM0Y6oetdmv0akyq21iPXL2ncVuLh7iM+ffzHjDf3GWSJt5+7SWEGPDq5z5FNLKpjdq4XjKYZRXvAJOvcbZTMNuQqZ60sICpqbyAzLHSgtZoiGfKQCCqSW+mWWmt5dXCFd58s+Ff/7J9x/bVvc//JHl9/802G5YimaVkbj2jrBqsNhERKQXjSyVNmkW/cuc5WbvnLn/yUa1e2SYuawWJKWRY4DbU2HPqGjczQTI842nvGrdEdspSwLjDILFmATruvO7/nqaj9dnGKdPX5/y4Bvg9uq1orAp+cpz5epF/S/V4lgTwiUYbNtPRvNEEw5u6C8yuzEtLPkYq56TjqodsvH3zna9yVm/312alwys8Bk5LMqGhYzlb3MJSQ3lExSP2wwjAKIdC2LVnnttXvW1mWaKWoqoq8LLh58yohKOZVTdM0FEXRwTqKGF03e9GZ9ViZpp5OTzg+OqSppiymM1zTkEIUuCglkg+EJFl/UghP3yQWJ1OmixlaidRBVuT4lISF07HtkpUqpK0q9vf2ONrfw8REvahp2oC2mVhwenFxql0jCq9GKupqPuf0+Ii2GDA9PYXopcKP8W9H5j4YDnnr7a+LprrStHXDxx99xKAohLHSjfLSCf0MipKyLEUgTEGyhsnWBm+++Sa1a8mbxGQ8xnvPYrFYDoD0K52s9ltcv3aN+WLBzqVtRuM1siwXaMZJaUnfUU9BRLiC4LExCI1REYlefBrbtsG1MojU1C3V8Smz2QzfOG7deRFtS2LoaYpn2YzpWEJVtSAET1SuYw1lWEz/xGXmnmJE2bPgfcaIOBs1Xx0yWaWy5VKMs0hSboeQyBSoCN6JImDAcGgDQbcMKcjrROYtdVA8mc+xs4prteV2k3Epz2lTw2z/gIdHR6yFkstrl1GDxPqVkpPTQz46/iuyMKH0OTplNEVONbTY2DByiU2Ts24gqMBcKZItGRRDUm04nVlSyPBqRIiaRXRUqWXgDBuppIk1BDEyaRaKteGYV3Z2GE4y3vnsHn/0H/9jdi5fFhccHambWnDnLqsSLQ9FE1rqxZSyHLKzlvPCpQmT1LJ1ZZvf/tprtEozbRpqDe/euwtVxSvXrvHqjesMQmBgLcZ3AT0Tkbu0ct7687J6M34ZzLDKnDn/XPWFf3dxIejpiauwzEVZgxQTJgntFy0hPoRIVILCRJTIVaOISvB1nQQetZkRSM95tOmUTJVihWTYNc27YUJYBqMUu0E3040FaUvoqnKdEso7mXCOSRqIXebf789qUO/3ezgU/4OiKMFo2tZzOp1S1y15WTAoB93UqPSZUopYaynLDi5xjvl0Sl0t8K14r7ZtKz0II7+PIaKDMF4SAkW1riH5QHReEoai6AYIRbY4RpZNYN860bnygeAcTVUTWofYOXeezq5DBzoV2dT1UWQYykMBrnXE0C4Tw1+XBHwlgrvRmvXxWFQglaKxVizwbEb0Z36RxnbG0Cl2HoOiw5JiENnV4FFROOn96xoZFVzeQD0/drFYsLf7lOnJEQdFzmI2JyXwQUrFkGTKzAdP65yI7odIWzU8e/yE2XxOvaiYnk6JJBrXEkKN946mEfel2emMnzUt0Xtu3H4BTE6MrQx3rAR57x3WKgaDUqY4VQ/bxCXjIXRTfiFErGXZTJPM5zxXule7g7MLQGuNCQhbyGiSNviY0EmjfKKqA6ep5uDggEfNIVvDMbfTGlkbuDnc4N2jZxgVGRrL1SLj2+uXefX5F/D6ReqjAx588HOO9k/49LOSyY2SU+OZq8joxiZaX2JdbXN9eAkbcx4/fMz08QOqR8+4FBUP4oyn+pD5liUf5QwaOPh4j5N7p+gqh2jwQI3DW6BVjLMxdVgQ8Qwzja4DWXaf3QUMnn+ON7/zHS5vbOCdI1pFbmW6MCYZGhNqXMLFhqgiwSSCCpSF4crGGj/70Q+58/yL3Lx+kzYm/DADk/Haaxt431BkmoFS5CFQdA5hLZFGgU+aYkUL6GLAXg3Q59Q4LwT1PojpTjp6dVvFonvP317Hvz/f/fNWZQPkQflfiuK7Cp0TmjXkgwHRg89ECbGOLQOb41qPUZZO74s8KRG3R5E0ouGvenhQLsql30IUBo9WmswWKGOYN5UwRJJHK9PpxAuNgQixi0z9NGmWZUuqcH9MZeFKeNcwGAxpnOP4+ISDw8MuWcskZhjTTZhIFq07XweVktzXJEiRIstw3fCXUBERCCbLichiGZU0rbXW5GWBdXXnoaDBSkKooyFTFoUkDjEFoWhbS1aIDILJLEalM06/kUXYZBmh85SQj5CW8JS1BpUVy+P6twKWcW3L0wcPl3xt17bs7+5R5DmLxYKD/cOumdplAghvVisR5UIpjg4OyZQmpMjjBw+oFgtSVyIBuE67oQ+Ax/kBdWjYPzggOtHiRmmcF3U3ay0J1XHUBRsniMVWcg7tPbFtiU0jlLGYCEGhkiE3GURI0XG4+4R3f/ETTGbYuXoDkgeSYH9G432DNpqyLCAlmqaRSd2iRCmDDkHoDEFOprGd5nrqB0fUGTbPl2uDCHZvaH2gyQyVVl23PgfveXoy5aO9J3x8tMvDeEyREq/oDdZmEYqM6yqyrjx+vs/NwRqTsODJRxWtitBCNc9ompyp96jjKfUgMc0S46sBtRV5sDhgdhK5wTr+4ZzTHz+hevCIKi0onh+xf3OL6uoQipJNt878WUEabnJpcIlNtSb9CqtoU8vUBfL1TRpaYmgZabCLBhsT13cU9rnr3Hn1VYyO1CHK+TAwbxaEKANweZaTOighaCDlpM6EYXFyTHV4yK1vfZuiqch1RlQGHb1UU+WAOtaoECiVQvmOm63BEQlE7EqWvgqPwXmlzlUO/Oq5W/3dxXPZ/9zDFUvtmLOS8Nzfrw4b9bRDYdh0ukZdLyslTR2gSZqQFI+f7fL0eI9Xn3+RkbFM8oLkXKe3rlApopURvnonkCcLkuigd0BLB0NKJh5MQSCiBiMa33Y+xQoVAkObo7zcp/3AYF+B9JX3KvzYHxPnHNPplIOjI57t7bO+vs76+jqD4bCDHwElidJoOKRpPClGGtcQ2opqMccaw7AoYH3CYjZD0zGIQuzWQrVky5jMQlAMJmuMdKIcrzGYjAkg1VAMRIQqmlCojkaZDwpUZiEzmDzD6ERA3OFMlklyahQ2Ggy5wLiZxbuWwWjIcDQmuBqtz0TPONj70rj6lQjuwTkOHj2RTnPXiDo5OKAsSxaLmqaVVSzXWrrZquunpUhsWmnIFjnzw2Ma7zg5Pl6qQvYlXV/aLpuLWnCw6Jxc3DGhRBaCLoUnxbjUy9Ddl/eB2LQi05sSNgm9snUNUSmM6hpDOpK0Ixk42H/Mz3/6Q9542zOebBCCQ5xlOiGvFc0OH7yIHBlpKLESFOJKIDg3Raj0uXFw+bPz5TuAUwo/KHg4P+L9J48oyhGv3nwRG+CDZ8/4+eOHnJaKKitpnONuM+dGkXF9qHndBTbX4OHBJ1QP7vGucqigyPMBmR0RoiGblNR5Yl45mpjTFmP0IuPqzR28aXGV4aCN5DtX+Np/cJn1p8fc+8kPGV9ZZ+21Hd63D/BZi24L1jd2UGuOjdkaw6bERkVWaBZO4dqastRociI5ZdJkVUtqG/JBpMwGbGxPOFYekxmSTpwsTjg4PcBHz3iyRnNco6Liys4VVDQUaoAJER0TzbTCpMjO1qaYmMeWZCxGWQqlqEJL0qFTq9DY7rpIMaFtjrZGPBy7bTWwn58O5lzWvgrRnAv6JFT6vKbMqvyA1l1wXYFvViG51ecJT9uiQhI4JEWICaegIvH49JSffPgJHz98xGF9ytdOZ1xbm3B1OOLN554HojCYiAxzK9Q/cbHvFqleltciNnmdPIZSTLXi8HTKdDFnOBkRomdzbUKRZbQJdBD/0P4YXGQYXVzkvPdMpzNAMRgOef65O4zGaxRFsdTw984RQ9MJ68lcjS07BVWtGJQld+7cZjIecJhbweWTQCTBiQG4sYbxZI1yOKAcDPAe7jz3HLOmphiN2Ll+jUXb4HxnmRdk9sRFhzQnInvPHvPk8QMmkwkvvfwKIRlciPiuDxCiLF6RJEykBCeHh+w/ecLlrW0GZUFbzZaZu3MOPv34S+PqVyK4p5SITYNrGkKK4pBCn4REtEE46iFKdao7YnfnPp5IDLOMQutuwlREtOTioCtrzlgmIOWYQUrL6H1nEOKXE7KxqxIsSuiUUcTL8DLApGNC+QhehlB0SKgui0nSF8IgcIB3jv39p3zy8fvceeElcK2UXDHJMIOGpmkIwRE7uy3JylkOjKjus6flMTuDZVbx1IuqgavBfR4Dx3XLL+7f5Sf3PqUoR5xUju1iwtO2ZlZmTEtFW8OVjW2mi2Pe33+Cm7a8pubccY7v3MxIb19jNp6znhcULiOZnEYbFiR2FzP26sCT0xr8gOgci3tPydE8XSR+elLTRMMOQ94gI1vfoGgzrqpt5tkJzh6zkzKCgso3DHyijN2+K3EYGqrAxNfMkwJTUsaCHPkcKdUMihIsJBuxOuNkfsqjvUfM3Zw6NOjjpywWFdFF6thy59Id8piDb8ElYuMZ5CUxRbzyVDGhCyON0tiwSAFnEkVuiLEfquukoju51lU49Iuy9O4sLmGTLwvsq/fIF903q03H1cf6a6DP7s8F+5TQHZZukxJ3RDTaFEyj4p3P7vE3H3/CXGtOjaF+9IBhiNwYDMEont+5xEYhEFetPG3yKKxMYnQfU6ne9IOz61LBnmv48Ucf8OEnH/PSqy8RY+C1l17i1s5logvkmRGOeOokuleu64tSCLrTy4ck2HuWk+U5rfMizd3dC3XTQGiwWc5iXuG8J/OWTEGeCcGhyHPGoxHTskBntpMIl/Nic3FuM10vJcSIKUp0kZMbsIOCYjwktgbjA8pokpcp1za2oi7rHbNqRHE6JB8NGKyNScrQuCC2h3lGiBK7nGvFOMZHlJFgr7RGm66hzfnF+8u2r0RwJyWib7syUTrw3gcy35VUSXxIZbIS6Bl8KXXUK5bjvovFXCiGdHiV6hLx2DkDdRlET1hS9HRJqQpAutSxGweWTW44mXh1BC8lZ6TTyrAaW+SkIOYhKUXQlmQiTeXRUV7z4aefkZzn2tVNjIokFRgUhWiKIzx6YxWZMVitcMmQkqETRUeI+kEGHLTpbNjEDi9Dfo4EgQUwhGSkRI4apXK8Cnzw6Sd89ugxw81tDg6P+ODuJ7xy9RbP377K85OXuHd6xLuf3eVgdspMtWyMc/LDI65ver735joju8fxKLBbJkZ5jXWHGBXB5pwkwzTLaFKJG6+zsfEyiglhljPavMm96T0+ON7reNRzyho2TcPpfM7+z99j/BpcvrGGrQM+OHxq0SODq2Q62JiEsgnrITeRtgmM7RCVWebOM8pLSgXZeI1Z6se64eHjx/zkg5/TGo/XkfF4JCp80wVFlnNn65ZM7KZE7So8jmyUozIlLJLUiWFp8GImQIqBtk2dd6wmdXi3SprovEASUW7wlBS9OYawoPrsU6iBIEmLQlhZckus6s+kc+P98ncrwl8qdhS8iFId3VKfST8vhSj6TDZG2S8VUIjPJ0pTucC7d+/xwYMHzDW0RvP2nTfAKj757EN+cbDL4V//Bf/ot3+br127Qh4dxnuUjuTOdNTeRM+7MdpK7yoZnNKcLhp+8OQ+P/roI/ZPT3j07vtc2bnE2tU561ueteQpVcLgaL1dJj7ClQ9gsu4ah4RMctosZ31jk/XNCTFBXQdm0xlZVqA6jn+1qMgyg689Te0xGBEuM1Dkhkf373Lvww+ottbZf/yI3UePRQsJRXRe4BIlPqjGiEdx0pajvWcoaylGY06PjmmD7+CkBFHmYNok/bLkW47293j88D7D4ZC8c3VrOj690nQVfRLKpnN41+IWFU8fPqI+OOTRg/u4tpaJdx/+dmDukAjJoVQSvfPWdTeBRakMFZ0MEiAcXED4vN2IdVJgixwMNKGVcrdzk07dQrBUHeg6kFFrnI9LiQDlpaFEioSUhJapOu/UKIFXdaLuQSnxr7SG0FUELgQRAephnKQhWXI7AOfQ/x/q/izWsizN84R+a9p7n+HO12YzN3c3d49wjykjIzIjs6asbAmkaoEQPCB4QQhE8wDihSd4AanVbwxqCQmpEAj1A0MLITFUl+ims7IyKytLOVVM7uHzZOO9dudzz9nDmnj41j73emRGZFHVoMgjmczsDmfYw7e+9f/+Q4xkP3DwyRe4vGBrZ860rpk2FVZpAonaVkQlYQ8qRbKqyMpAoUVqEhqxzRXzpSRfSZCzYvAedCCbhFcQouLo6IyDF6fcufsKW3tbvLg4Q03m1Jv7KJ+5cfeW2KGqDr2MqOcvyYsLvDMMG1O0D9wdDCYccLbVMvTPyJOaLTMnKFjZyJbqUT6ghhmz5h0u/JQPXkTc5QaVVRw8vWC2v+Rlm6nzBt9ImreGQENL88Yu9955lbq2tOmQuk64WqE2OxazxEk7oKxm3kxIyxUmeOgDB/6ClGCyUqxmPYeTzK1QkduBEDVKT7E5E3Kimc1pMzw7OYLa8vruDsfPD8kXHVWyTGxF8D3VVFw8q70Jje2hUYQw0JgJ9BlXK4JBFNK4IrXXeCOB7GAwygmbypTMXFVJYc96jbWPPGWBZDIpe4n9S9LUWCOBEmMTo5Qiq3JdGVPgH1V2qcWaQknRqwpUM9obC6+9LALXdgzeBPEqiRDREhoBHJwcc3x+hJlN2J9N+b7e5qVLfJChnU84UInPjo95+84ddFKQIq42NB1EY4hWE7VGZQNJobF4LEsSHx8/508/+pAXbUdna877zMXRJXtHF9zZXrG/2TDxS0zsiGYf4xxKJVIOKCMirVz+JCUzuNl8kxgT/SDYeN/1DL0nJ7EyUVmj0ayCwiRN7eYYNEpnmoml78744Z/9CefPntAsTvGLJWq5IsfEkMI60MVohfbd2hgtZPDO4iYzFidnNEOiXUPBA6RIZS2XKYrFRPJ0yyXnT57QuYpNNLau8THjQ8YhSlg5PcXmJHpJdzs7pSfTnZ5KjF/+6mzmFz1+RYp7GeiUrmSMictppEGKbegY/qBAcjqLgE4XUYJMiqSr0fqa6o9c2CVf3RorJYEdI2WMJJ2+dGn6Smo9YvVOk5PCiMgOo0e3ODBGVH055XIRUrBNGRAoI0ZKfhg4fHlE1Ti2t3ZE2FL46kLDFKqTbLliGUxJ57KGlFIuu5NIVWWsAWNiMSMTiODF0QuePH3C8clxoXWeEDa+wcYrNzl49wOef3ZCripOP/sU07VUSgMVXVTsUnPbG9SQ2O0rNv2EGxt3UKotUMWKVveE4Qar4RWCqVE0XDLBNq/yzsNtuuGYH793QKtmuNjS8R4qrNhRBnvmWZ1esn1jg42JYbU6Ymv3FmZVMXx5yuqDE8yTxDd3XmMxq0ibWyhr8Een+GeHmH7B0HXMd7cxbsb0zh737myhvnhJfDGwqWC79VzWisWmY7q9gXWWbtkSlpEPFiu0z+xON0nKMmThVi8XHUMcME2FixMZtBpN1MK8yoXFpBGFstIjHDJi6qXDVBSsWQkTZI2vF0hBX1HdFMIACyGKOZhyaCwGW3afrIOp5fKWNicFBdmuTboEElJrWwNQa5bZCFGOuwFSYpqcQEgl+ZScmVrLb77zDkdnp5z4QINmx3o+efwp26GnUom7WztsZEfqADtj0Ik+ZUxVkaxh0NIcVdoQuwGjIRM4fnnIxz/7IcuTBdoPfOPNtzg5O+fi5IT3/uxPMYfPeOV3f4dLn6lzJc3LEOXzKLFt1owNWgIVgEQXAsMwsFqJVXcMaT1ri8W0rxwOqS1yQOWcac3BwSEffvIx07LbX88/xtmIFj96leTcx7KLzxQTwiywXIoC16YUSyayNJ8qxQIxFwvuwjYi5eI2KYZgxhiyEoZeIq8H4CJSk5qSyudC/auJ3X4lirtCYU3ZguVYug758CkGOYAU+XMWnwwhiUiBdkZTuZGhMuI2V/j6+nWudqfr749dzkgXE0n/FfNgPMEUEdMo/pFBqCm2upJEo5whmZF3LlPzTEQZyWL1IWJrzWQ6RVuLNhbQdF27xtp934l3RdPIll1FIMouhkwICZ0DSrvib2+wOmOIQp/M0Lc9/eWKy7OX1K7lxu0tTk9/Sn7/hJv7r/Jf/NZrHPaRn3z4Ef1qiYoDVhsGr3FRMe87NrXDuprbwVKf9dhhxuoMVNjmPF2ysJnVecZObqFvv8pgprQxE4NiZiPfvreNW0FI2yR9iNm4RLsb1Mc9+uUTtpJH9Zd88tEzdqpbpPoca+cMK8WUDTbmNVu3X+Fo6Onu7rIicvu1mwzvVxz8xbuopuKIjs1Jw+Zr2xxMEqsvTpjaBZMtxdOLp7z7/DHPtS9GKJ6JlSQsFWF/d4/f+o3f5uG9VwmlMzs4PWYIK7zK8keLOV3IctMHEjlK8RLjJhl2hrJtR4nqWGsD2RRaoLgVjgWFa117Tkk6y9EEyhj0enRf1J3j1lNWDEHktBaOdB5nTyMLXoRZskNArkESojC9hvPnhO2vGp+sItpmbFbc2ZjzX/k7f5c+w49++CPi+XNumcDXfuO7LPqBDTvhnVv3qfsowEjd0OdANIaBzKpYcUSnqJxds3KG9pzTw6c0apOdrS2qdsm379/hZ+fHTBU0WRE8JDNHGYfOAn2hRh942WXo8ZgUCmOPWBCM+LPR9pp4Sq2xeZWTKFO1lecmsVwtZUaQJT4zFWqP1N60brpIV8lnkm+q1zVKlx3WKARUiGOkIhOGYc3yIyVUSqWuZcmcDcUQLV1paXKBg3OpP6nUsKxAWyPJcvlvUHHPMPYPpAwpStGurMUZA062q8mo0sVcK9mlMFvngCtWAIxsgsIjlilE+ZUiGMki+fV+IKtyw0qrsV7pBd8sjAUl3U9mlEFLtxZCKDxVsWaVgWwkJI+PAzFHTGXYnFZsbs2ZzKc09ZSEou17+mGQRcqKJYIp9qTi25zWClQxW5IryRq9tmLIOZab3aCVQ6XAvdv3aJqAa1r2bjrOF7DXWTbsEjNveHq8YMccE5oOoyPi1OAwboKrVkyrirNs2VnOmT0LPPv4hB9/8ZzN/pxj03JsMn51xt5riju/to3fjnS5x4QVle9xquLWtiZWipzP6f0Lhs6w4R0P7+/zZrXLpWrZ3Y+0+4Hz8JLJdMpqOqWdwsOHb3PYKf7Jz37M4TPFZG+Lv/uNb2Nu1KS7OxwevGQRA2/fesh7Z8/54599yI3zgXtKo7oDjg6e8PGzzzmLAwqYTmru3drB9wMb803eeftbzOYzfB5Q1SaVq3HTCacnZ5y1S0xt8cU7JQcJOs4hlfNPwUmLZ2fKaCdqx27osLbC2DlaFcreWJzLtXdVaIXxlcfAiDwuDpQ4whFzFy//nMfBYjHMK89znUCiytqQS0GPuWQGwNqLSSmFCrp43CswWYRFyTMxGu0s1lQ0r77Gn//x7zGdT/mN+3dICb742Ud88JP3mBgHVtPahJ7WTLIl1obc1My2d9je2ObO3k3JUVCaW3s3+c43vs1Pv3zJdDrl1s19ZpMad/8Ot3f3+ebXv8G0nqKzoQ0RZ4T0ICO1It4r0Xyyk5aPL4pVu1aw5nTFtJEmqzhIao1Fg0+sVj2rdomdKDa2Nnnl9dd4+u5PSNcGuOtdUznAmfUplH/HJLO3mApGHmDcjalRuCVFPxfSRvIlYWnw+K6XGZur0IgzbEzjrqssxuoqmSpG4cr3Q3tVN/+aAv8rUdxRkJUuRT6vD6xYhF6tdihd3CjGnqR070avB6fx5+hSohIrnfyIX44du9FoZ0BTxAziPqeV3Ai5dEXGOKKPVMYQYsBWFq0N1aRBVxadguCAXoJx0VaGWx5MrYkpMJ1NmUxrMXsCtKnwxS9eGYc1YmTUDwN1U4OR4o5K6x3K2lJVlUQmDSEOZGpS6fYqO2VmG9A9O1u7JP2Si4sDjO0Y8gXKeo6efs7R0wN++9Er7O5sk/IlQ7+iqWu0sqyahFEdF6sJG2eOL/+4J7ubxIWmP9ykmlbs783RdY8KhidffsHzgwWd7pmkzFY0VEqxiCtWVSCsTqmBxuzSrLY5PjCodMLkwS55a4NzFti5YfA9b7/9PaoH4MKEz378MblfMV96eHnCzz57Rr5ccos5k2yolOX8p5+xqgP3NzQ3kmMzJny/wt2cc/fNe+zGntViwbSuqK2jto4YFWenzzk9PeLW3XvUxjDEhs3tLdq04ry/YDqfcjl0NEZhg6WyDUplrLbEEAvX2hKTcB5j9JydH3F8esR8tsXdm6/gnCuQiEAq1/ntMgCNRCXDV1JpWhSg3brbXo/9R58kMeQV3nbO69jF8SdzKWbXvWqus3Ck608MOZG0eAopo6AI42K/xGVLkxKzAlu6lDBnZ5w8eYL/9HPe3tkldS2ewGUYSCuFzQ1n554FcPys4UWzweb3fpu97RuEDFsbt/jB927z5htnXC7O+eTjDzl5vuRvf/977G7tUlmLDQIr1q5G655olFjiakX0mUGBK4pYXXYrKSa871itVoQQ0Uq8m0ZYI0aBMrxO5CHiV17gXOeIfcZNLa++9jpPP/gZGIOyxdLEsyZrrEVfXHEbtJKaIUPwK8grhiDurU4Wdp2ufkaV4Z+ysj9LSdh2McTi05MEco2SVaF0IZfEyBB8qXGj+FF9pc79VY9fieKulBbJNorQ9ZJEb+SkJhAnRmOJpWOutMFqjdNykGZbmxLqkSPRjBCLwnsvsMmoZivYVsoijDC1o2HCbGsDU4QSprLrQtqHodxQhtVyJXYERmPqSoZlzgiE44qMWmWGmBFLpIRyoJOmqibs7O1graEfBkgK5xqMsUwmU3IKLBZn5BhQWlPX9VXnUXBD2QqaaxSzSEYCfKNykCNNNSWkgsuHyGXb4dMlnT8DvSApzeet5/z5OW/cf0i9s0mnl6TcYWaBQMCqnqwHTNTsOUtcLmlsRbN/n5dxwr1tzc3XHtDtNmSTCdsDH8T3OeUUWxnqvIEZpjRNxe6NyKl+im41m2qL2De4Q0vvL2FzxvTeHb6IZ+TtDSJLbrgtXExcxJ7PXjyjT5e8urvJzdSwkcTi+cItaJgym+6Res/2oOimhqPGs9UNnB4f4E2Dnc1wypGVp59PmdSOpqpoqgaUZrUaqJo5de1YLS/ouo6TxTEnyxNOLk84ODrg5OSEzXqTB/v3md2ckYaIm07IORZLW5mzaK1p+yUXFyfE1NF7zdn5S+7erUtxtbLlT8IoAUQYR8ZYcS7VSq/VisYpYvCool9QWvKEURltxa5CJS2dqrrKX5WdZ7WmP1KghFScR4VOqqTj9qW5qS1DHsRrpRJHzq4dMM4w3ZyzfeMWlsTl0QkvP/2Eh9ubbNDiGg9K2EcCrFsudc2zdmB65x7Nxg0+e/9jpu/M2dzaFVweuD2rWakZl/MZrYLhbMHpZcekEduJuqoJxoINRdKv0HUlpnbGkLTBZ4Uu3HAZmI51RARNMr+IeO+FqpqBSkHIBD8QfMIhDeOy87z9zjf44T/7Q0la0mIMlspCSEpraERl8WZKSOiO956kFLFQpzEGUvHRz7k0rSW+MFl0XQvUUiI3U4yiNxjhX0yZx0SskwWeLMr8n89D/ld5/EoUd20t2zduiDVnVaPdOVnD5uYWy27F0EuRdVlUX1absnLKMFNZg6qku8YZJtMpCsUwDAUTLcb5ZeVLQfwlMJqV7+mSp1aakDzaix1wZTXKWYwWXHtz4qhtRdKZRbtgNp9z78F9Wj9gKkdIEd+u2PBTYoy0fUfOmclsirFGsPC+B2XY3tlhZ3ubuqpAJc5PT4TNYzRVkSaPPnwKU/D2MRWn7GtUJOWED56jiwtihts372ONhJ1UNaQucXpyiTLQ9QO92+TkZc+bu19nc3afsIpEU9PFTEwt2/M5i4slK2W5YbfIi4qTjxc01Tbzm7ep2p4tpfjgwz9HP9xm7847GLXLprvBoxmojQk57zC1dyTgRL9gpiO7O5opG5wd97w4fsGju6/x6qO36fYbYlQEOioPajrj8cUZR13Lj794j53QcG9miRdh7euzTJ6V8bxcnZJXA4tFpNrdYJUydR8Bw88+/YynveXOnRusDg4x1jDbnJI0dEPPzvYu21u7vPejn3BxcclGtcNvfOtv8/izLwg2MJk3XLaebtXSHq+I557l4QWvPXwdPZuhlJFErZwwxqFNol902Epzc3eX1WrJEC7ItKRkaJoNhr4Ed6QCsSH+QNEYbF3T9xJ67GxF7yWOMHiJV6uqiiGKoE7cNFQpXiLLt7awa5LCpHEHOrqPjjRMsVu4uFjw5eMnYCdYZ3ATR1aRqrIooLEVE9dw6T0pQRtgYjTvvfc+r2zvoExAWxEeWgITBclHVqGnT5m6nvLaK68S9IRhlfn0Z+9jrWM6n4GGumrxIRIuLvDdwEfv/Yyjw0NubG0wsQp8T2UUbj6nD5H57j5vf/d7TLa2CwxWdANFdSvQpGE2mxUrgrJ4prweqOYMySbSEMmTRE6aqMRT39nEzddfZ+/GDVZHL6nWdE6pFXLMS0CIliG6GQeuxqCdWBIMSYRO2tly38r1MQoPKRCYGIKJGSFaFoaYkc6cvKZFi+VxXENCWSnMeJ7Jf3M69wwErVh1PUOOdNHLcKl2KKepdAUZqiQTaGes+NBohasrHrz2kFcevcrL0yNeXp5jLhNk6LpOAqmLP8woAsgo9vZ22Xtwm/p4yr1XXmEyncjgRYvLpNaaoVCbjDb4IdC3LSvfEzTgDNkZvBcZ86prSe0F7eoSlKKqJ8znm2QU1lUobZhviCx6PtuS4anv8UMHWkvQdxwKPznJjkVblJLtv1LX+O4qY60i4Xn+4oAXF4dEDc9Pv+TrX3ubED1p0bO57YjnkX7wxGzpTgfii47j5y9I6YIqwaU/I00TOFCcYJND1VOeXp4ST3qmreVec5OhtujdhmZqqLNl8tqE+9+5x7nP7Le3mdNQzbb40ftfMn+9pm4q6py5PdtmpoCguflgk4sP/pTOV0x27jDMNU3MTJ1Hr44JfUVbWcxsA7exRfCW5vZtJkxRfULFyJaGpCzPjk5Jqx7/8pL9V28yu7PD5cdf8uLjA56cXPAEOHlywtd37/Hp48+JtSK4jJvVNO/cYOYd5ycdl6crJptznn3+JTvzDTZubnK6OmVjPmVzvokNmsWLcz56931yH3n7174NZeuvtSKmQIg9KQWqyoAKpNzTTKYslkeQnCRsZYW1DVpZgeNCICtLjFrCS/QcrTW+rN0X5wtiTDinUKrHx0Bd1+ubG2VlYYxaXFCteKI7MwqmpDhZWxHCgNKW0+MTPvvsM569eEE122BjPmP1ZIFSiYvzc46PT+Q1K7G7NtowLC+oSbz96n16HblQmmUYmNczLGC8UDdb3fDliwPe+M1v88c/fpf33v+Y/9zf+V0ef/4+VYpMGwtkhtRycrHEq4qN3Zu88ebXmFU1WxXsNRAvj3AEQugJxnJ2+ILDD97nze/8OjEpkrZgCxNFWxJ6vTAaY3C2KtF5rGGZnBPJJGJShb5qJFIzDwKfas3ejRvk1QoXO+Gz96YwicRoTpUZXCqBHGDwJJTVZK3Fr0lRBqG6ZDWkNWtO5mGgnZO5iTWlKZSTVbt6TVsd7QVG3vvYtbuqEn+bsgBdj1j8qx6/EsW9rmte/9pbnJ2c0i6XgnnlzMZsyum5+Mp4H2iyE7riOMUulMNmNmVjZ5vzYYWbTZhpXUQEDp9kezbNMhTVWpfVWIPTBJ3o00DsEsZZhkFMwpRSdH1fREmK2lWQMpfdimW3YjKfcnZ5wfnlQl4jRay/pGo0m1s7QsuKic2NbXZ2blDXE7SRCLfVsiV4Tz+0tO2Srl0JnzdlVFWoc+I6Ubj+wnWXxr2IaWLPYnHKweET7L5lvtHw4vgzqheBxfkFOXoePrxLT8fx+Rlb23NevbPH7Qd7PP7Tz5itMhuTTXZ2d9h+/S4fPH3MzVsPePuNb/PJTz/k4ocfcKO22DSgjRWo5PSQG2aLB99+m73feo0v/BM6G2i0ZnvY5IuPn9KdH/PhR/+C2/fuMcuG2/NXmJoGPWn44ukLHDtU9ZQvnh0T2eQ8eN54eBdz5jCxYbve4cnnj/lGfZdl3zKZbLP3+ptUypLagI4JkzOr5hPhec8X1G8+YOOVWywuW6rPn/Jg4thsDC8fH/Paa2/Rn2tWdeKjF5/jssaHhq4zxN7h0pzf+u7f4i/+7M+INrK5/zbExHQ2ZWdjg4uDcyplePTwVV6+OOD26Sm3bt+TXaZ1pBRKEyBToBA8zcRQ1YmYVpBrBr8iUxPCIPMcZVDK4AMkNSOEwOnpGZeXSymow8DLlydrSmzXF0UzgrFnsmT7pkhd1dy+c5v79++zvTknI2E3dV1LTGXXo5Ritep4cXCID5FvfOObHJ2e8ejhQ+ZNw6Su+NlP3+UPHv8Rr7/xJm46Z+U9Xd+xM29I3YqTiwWXFmIeWHUrrNLUqqKJGh0HzlLHUM94fec2Tz/8goOLC44vjhhWR9zd2+LGphAGFnmX6WTCrYdf4+jSk7Lm7p17vPj0J+xry7ZZMVUBp+cM2rFTTbg8OeXwk0+5+fAR1cSJkWLKVM7SJcl7daVo+mF0R2VdAGOUOUXOI8RZmC+Iw6WPA/deecDzTz5lWrkCywj0qZLcb9dtHZQSv5iMiCNb39OFgWnxhxqngQpNLkaEa2q00cQoUYbaWkmR0wpFElfYnIWNlRLOVWvW3ujbr/SVPcPfiM598APPX7zg4uKCvm05ODykqWtWyyUXi0ucNUQfyFkMf0iCF5rKkSvD+WrBF48f8/jJY86OThh6I1PsGMkKfAzEKB28Ll7nwXvSieX09ITN7S3qZsLEGmZbG8IuUJnKV0JNykJ77FYdymqqSY2tHc5p5rOGZbeCkNjZ2KRyovbzPtE0cx688iqT2Sb9EOiHQNf19IOnb1eslgva9hKjE1rL1N3oCq2vwrE1IysGyDLEMRpWXcvR6QvaYcGmmbO9u83lKvL88BN811FZy7s/PSCHROgHXKzxtmOVz9m+N2ebbbRqyJMJX5wc0lvDR0+eMtu9x+buJke549bDR+g+cPj8JR9//D5vvvE6z979mG+8/T1OQ49uFN1iwcVRj1sZPv/4C974zlv89IufsTd5yH6zzzRPMX7CchlpzyKaCYs2El6e0tSaVbvk2B8zWXiG5QWzTc/FR8/Z3djBLzwvzh+zuugZQiJ3nplyaD/w/OyQVx4+5ML3/Pi9d5kePaM5OOGy71gerNib3GEyv83mfI8YPuPJs2fMNja5e/sVwiLz7gfvMywCxpdhXTcwpJ6PfvIBOzd38Aws05L2ouXy5IIbr+zz8uiEbtXx8viIpy+eMdvaYL45ZQhLQlgSwwrnYDqr6UrAQ/QD/ckJRk0JweB7MKbm9PScg4MTloPQ9+pasndPT8+4XC75rR/8NpPJhMePH1PXDbs39nj65Anb29vs7u7y5MkTptMp8/mcZ8+e8f7PPuT8/IzV6oQbN27w6qsP2dzYwBrDbDrl8ZOnnF8seeONrxWYMHH88pDNB69AzMSYmM03+Navfw89m9Ih8Ee9uuDLzz7l5vY2N/Z3qZuaqGC56rBYTNCsLpa8/+XnzHZ3qDZ2+c53v8/WfJONrW0OnGMVPMeXnpw8p73iO7/+A1S9SRcW/PBP/4Rf//Y7PLh7n9PnHzPfm5FNplZzTJYue3Gx4uXTJ9x9+Cp1pRmSwFsGgzaOHCMhxqIxQeALRhqhCL18FNvwnMYoQREsKsBax9vf+Cafvvc+mypwcbEQVloSGwRFxhpbkqcK5KUtrm7Yv30bNZly98FDXNMQggy7VRbO+2oYyDmhUkTFSNf3DMPArTt3mW9t0a3VzHHtg5XJOCO7heg9y7ZjUlXFEl0yYf8zUagqpf53wH8BOMw5f7N87X8K/HeA0ZLsf5xz/o/K9/5HwH8bWV//Bznn/9df+xpak434JxvnaCYT9nZ2uLxYsLO5XRJNAtppTFYScaUVqra4jRn7D+5xc2+fYbGk6RPay2BlZN6EGOSger/mjc3nc27cvon3PTvb21STCRiLdpoQPDEMKDw5e3IyJCyZQCbhw0DXr1itDDF0NAZ2NybUzgKKvhf+6+b2DbJuWHYJHzJ9iPgMqnZYauYmUVWZFDpIgdUgKlcRXljx16D4lkg4nuB0RIbccdkvWA6XqGPPw/u3+NrDh7TtJcdHR4QuoKlZnnV869XvcPTkmOmtu+hZw+b+DINDJdHTX/7kBbd3b/LF0ZfUYUk1Gdj57j75xiYHT455dpmYZM1trfl8v+aL8+dYbzF5xsYw5/npcz764ku03mU4qWk/1fzoxQds7xyWMOOaMEDbtjx8+JDVcsmnT59Tvzwmx8SX9RlVVZEBc9iiTc3ea6/hLhacnp6Suw4VApWrBCNOhjduf4293T2293bpuk66mHd22Praq9zLWfxDJg2TvQ2+v/E93mnfRiklCsZh4PVH3+RyucQPAzd293nz0ZtcnF9QVRVcgmrh7OCUtu3wPtJM5lT1lMtFz48++Bc8vXiOmmtmO1OqyrC/uUl7fskEh9YSTdjUUxQVTb1BTpa+y+RUYXSDtRU37t+nPjlmMpkwnU558MorHBwc8PLwkP39hhAGFhfP2Lx/j8kkcLl8weamom42ODj8hNdffx0UvDj4mK+99RavPrrDj95/zPadOzw7ueTDz5/TVJZb+7scHR7yztffYtJMcZWmrnb453/0x+zubLK7s0esHX3l6BrLly+f0erM7u4ez3/2IRfHJ2zfegU3u41VmtX5OT/8iw+5/9pD7r/+KpPdHSanL9maz5jGzNdv3+PV7T3aVcvn+8/ZfPiq6DiA6uKQpW1wfkB3lwxHLzDdI1596+v8wYsTPvj0hKqp2DGHqJCYVA3W1awuTnj/yw95/RvvoKuKPEScUvhgCsQZ1l35GCiis0Fp6ZxN1kQgloElMaJJOKMw2tCjuMiJBrGtQFmsFZWvVkqMyFLCx0A2DudqdN1gJzPyqmXQEIn0o/lXgjAk2iR2ApXS4DOtz1wuOmaLFdE6gXacIgVpCrOSxK6ORPIBsibbmnaI7O1s4NyMnC1NPQroPv3XL+7A/x74XwH/wc99/X+Zc/6ffaVIK/UO8F8DvgHcBf7fSqm3cs6/NDLEWsvu3h6Vc3TLFefHJ6KqpEDMI2vAKrEfKNz1FBOOcUBFyWANuCy2nNF7Wd0KhhWuOctZ53BWvCJGXvn1bVwInhQGohe8Msaey8slbbscPy2gqOopTeMwRhFyomsHnGvY37vF7TsP0LYhJNAuY2ItDnAp0alLkrFoMpcXPcnHK3xtFMQUjC8X1z50Xg9WN2bbzJpNTk5P6U7hw794gnZQVYbLywGdNRNTYZKl1tucHj3li0+OmGzVWHVK33kIYJVjtRjY26zpO8VPfvwhq+6EFAc+//KctMpkr9nf3yc3myyT4Z//+btEE6nsFIXDGM3NvZvcvXuXnZ0dJtWU6XQKKKbTCcHD5uYuSikmzQRypmtbckxUVUXTNHRtuw5X8d6zOd8gevHp19ow+v0oZEtslaapavpVK+k5XmLdJC9TM5lOMCis0qiUGVrJUJ1NpswmE2Eq1Y3g0kpz99Yd7t+5h7VWgh+QgVxd11hrqaqKDz/5lKeHBzw9PGTFwGrV8uzlAVrBxFYcP3lBWPaCidqE0Q5jKm7ffoBWNbPpFr/27d/g29/+NVIEqy3cv03dNPRdjyJy784tHty7DWRsZfnur32byaQhkvn2N99hNpuhgO/92neo65oQA7/xve+yvbXN4AO/+e232NrchBQIQ0/XLvmjP/wDtuZTnn36Pk1dkVKUmc1qhfEe4wN28Oi2I5ye8ez9Dzlpl9Tf+AZbTYXX4HKk0gmTE//8n/1TkoKPP/oZt+/fwkdpvOppLZ41VlNPanz0bGxt8MbXHtG2HdZa3v9wYFCOO/fvslwFHn3zO2zfuUe1ucurb38T9/KAb3zjHcxyQWg7TNacnJ3TnZ3yT/7oz2irOa+/8SbTes5lH0hEGdhOK2JMDIMXERmQclhTFEXMpDFkQhJ9i3TIVrj0KfLGo0fsKBEDnm0cS61JopGRoHHhwVV1jaskMOi1N99gY3+XO6+8gps09D6Ko2uSrrsXTStOKXLn2d3Z4uzklLv377K1t8OQAlEniFWZBxbf+Bzo2w5iZD6b0i1XbG/M8cNA2wrXPf2bJjHlnP9AKfXqX/dz5fFfAv5POece+Ewp9THwm8Af/zWvQU5JpPh1jXW2eCsUD+rrPG8yueDTa4Udkj7U9z2DH9C2XrMSxG3tivObige1sabEgl1Zp4ppj0A2Q98TBhl4DkHoVUZX3Lh5g0kzw9qK6XRSBAvQtitqa3GV5sbNO+zfuIPSNSlrHKoU9YCJkTR4jB7IKpCTwg+BFAKMfOaC7+kiXlFaFXsGSWKNSWFwNNUmjdnA9A6znGOcIneJJjqaqqaxU24/useN7Tu89chiNww4CENgOnfUrsZpB/uwu73Djd9+hW7VQi2q2s3JFlM3x7ci1GlmU765swOVok8dU1sxLDuh5OXM7u4Oi8UlztTkqFgul5ydnLNYrIDPCT7wrW9+k8ODQ14eHhbIS9LrY4pr9V3fdbz51ltcnJ9zenrK/v6+bLFzxlhLGDynJyf4r/c8efIEVzn29vbEDCol+q6nW674/Mljvv/bP+DHP/wh9+/f53yxxBhD3/coBW3bcXZ2xubf/Tv86Mc/JoTAZDK5EsRkiVarqopf/973ODk95cPDJyxDR8vA0q+wjTQRNZZNt8GjN97k9q2b9KljsbhkPtvk137tezT1nOAzW1u7VFYaAWczQzuQg6Z2JR3KGUwJbkgpMZ9WMkBXGioH0WOsZdY4UopMK8t8UhN8x6xumGiFpsdYxFcnwXfffp12ueDo8CnvPv6Ci4sLlDLUrmbLNWzONnlx+BIWlyyfv8AuLmkPXvAMxW999zvkizP85Tk63iQMnsvzI15/6w2+ePqEobtgMp8TkmcIPVkl2n5FXdegMykHnh885yc/+Qnf+973GZQjmIoeS3A1+w9eZbJ3k1471GSOm7VMdvbZ2NoltAPPv3zCm9/4Lrvtii//2T9la/8eut6gjxlrKlSMwgmnNIHrwp4LNBuEpRS9BGmoEnCvIZc81RQjn3/6GZeXC7SOLNolF+1SwtgHv64RSYoKVVNT2RrjLNX2nKOzU8x8hqkq+sGvg3KCj/QqSvBQzuQ+cHhyzPnZGc3mlOAUQwoklUheaM5ZifVJCAN922HIHJ+fsbpYEHPg5ekJ3Wr1FSfQX/T4N8Hc//tKqf8G8GfA/zDnfArcA/7FtZ95Ur721z/Ulcgjp1wS1IV7nkIUrEyxVvutVWTFb6Hve7quk/xTdeXeFqIsEjHLcyojZv9j9iI547QhFgqSdP+eoR8Yupa+b+n6JdpY7t69wXy6DVicrTHO0fc9zjm2tmWuPt/cYmNjBx8yMYnVV0yZELN4N4eICpmcDSkqMS/LEgysxlBvBJrRxrBWM6pR5agwWFSqeHD7dW7feMBsmGOTxseB+eaEVXeJUuBszbSZU+mGVx5MOG8vCSScDRhtyCGxHDzz6RRwPH3yjHbVQiPe0tE/I/aJ2Gf29m6yvbfNj979MckmJptT0uUKF8X97vz8nDfeeJPj42OstUynU+q6prIV2xuOqmp4+uQJp8cnrC4vefT66+zv7dHUDUophr6nsg7fD/zoRz+SQROKH/zGb9I0zTp0JefMxnzOn/7Jn0BKNFXFO++8w/n5+XpLDrC9vc2yXZJ8YHdrm7cePcIYw2q1wlphsFRVxe/93u/RVDXOGH77Bz9gY2MDYwze+3WRf++9nxGGgXZY0uZL3MyhdI1NhiEGNqcb1BhubuzxO3/7d3jt4UNsZaiqmrbtqKqmRKqVIZvvqbRG50TjNEYFoSsqcSy0CtqhxTlH7WQxyhmcSjjrqCpNG5MoUXOgNoaQwWSPMpCSp1IW5wBT87U3XuXLzz/j4OmXNM4wVJa6mbFcLPmLP/8z8TtRit5H/vA/+Y9pY2RIiU9fHvL4R39BZS0/1Zbj732fu3fukENHe3nOannG4uyI4FtCd9y1hQAA881JREFUuyS0E2K3QlmLjx6TAoSB86ND/sUf/lPeevQa/dChncHnSNaK1vcMKWBTwKfIYnlJypl2CDz98jHHL47ANjTbm7StZxiE7mqtIrYBq8H7gRgDzlXCI4+jeEsMuLzvSVF0A6NnlNGKvvN4lTBOc3JyzGTSMJ9YppsbtJ10zTnEQmyUoairKyazGc5W2Kri9r27eKOo51Mm8xmul7CRGCIMAyEHYfalhCrCRxa6BJMjrEBnMLbCGCuGhdYQwiDPV5T01lr2bu5xeXHB8lI83f86A4J/3eL+vwb+XQQ1+XeB/znw3/r/5gmUUv8O8O8AbG3OUVwV6+ue1kZr8WYpHhtKic8CRZYdwlUCvBSVGTor6olEcoU2oowhDcN6AQEwzhYIROLXKLJmSFhtCr+9rPRGr70mjDXEoEhonKmoaiexWUZjbE3KhmXryUmTimJW/oT1nzyEYnsQJBdxnaRTFrd8jWObri7UnBM5SBRXjgqUwbcDf/LHf8JU1/jYU9WWIXRYJzzph6++htKWg4NDmtkGrnYANE1D5Sx92/L08Re88frrPH7yBW+//TamgmbSYLDUtuHZkxccHrykHxbcurnLO996h6gyeojkXoY6T58+w2iha379a++sC2TfdfRDz+7ujmRTGsmFtdby3nvv8fLwpeRLRpHbf/Odb0gkmdZ0nSgPf//3f58XL14ID9w5/t7f+3uknJlvbPDy6IjjkxP+0T/6Rzhr8d4zm834nb//97HWkWIkxcjhwSF/9Ed/xNnZmUBxxvAP/sE/kMUFRfCRruv5vd/7J7SrllR2kr/9279NXdV0bY+PHa++eYOHrz+iC57zyxU5w6yecWf3Nq/eecBGPRUdRnTELjOxU1QWPnNVVesdojGarm2xxkqWQBCXRk2ElJnUthSnQTIBotgepzDQhUGONZBJDJ3sSLTS+ChMEZ9KQHUWX/L9O/f4WzdukUc7bCWc8OwlozOHJIP+4NHW8dmXX/Lez97DRrk+49Dxz//w93FVRUiRpy+egLP8X/8v/yGz2QzdZ6qqpqoc+/s35Ho2mpOTE84nU3bqBlYrTOw5OXjKVm05OzmgXS5Z7m3iTGRea3TssWnAqczh8yfc3rvJyxdPeOvmt9BpYGJBh4EUErWy4hJZ7LxziuQMYcyzzZALeSJHLxCvlqZPa1P8qAK+9wxdx60be0wbjW0qlDNCV6yVwCNFtV5NJtTTiVR6o4iAqRzNbMpscxPnw5UKePBMSueufMAmuLx9i94PTDbmzLY2iApMZUjBoAr8qIxmGHRRuWRsVYNRaOewdYUL9V/btcO/ZnHPOR9cK9L/G+D/Wf77FHhw7Ufvl6/9Vc/xD4F/CHDv7s2sin1vCKFgsw2hH4iDJ0LJFh39MaTDxihyjKxWKypt6bqO6Ac0hty2a9qjDwFdko1yzgzBY50TwzCl1oW89+IVAaXQqhH/1lgjhSvFjDaWupmSsiJmuemyNtJ9Z1W8ucXpL+VcusChwEmR4AdSGBjzVMWjRmPsGLwgr5+KD96VMq14bcSESgrfR/rVgNOK7//6d4olQSbEgY2tOR99/DFdt2SIkdcfvcKDh6+TybSrVfnMcjw++OBnNBPLzds7bO1MOXz2hMVLT9f2bMy3mE3n7Gw2hByZbW7w9NNPeP/DnxEHCH3i0aNHbG1tSfRXUgy95913f8gXX3wh8xCj+N5vfp/KOVxd0bYtSmuOT05oe/Gnns/n5JRoJhO6oS+cYdjZ3eWtr32NR288oqoqrHPcunWLg6OXMt7WsHfrJv/5f/sfUFUVoSg7d3Z3eXzwnIwwjO7eu8vv/lu/y9n5+Vq2v7O7w8effiIaBaCZTHnt9ddZLBZ47/E+oK1FaTF7i2lgUhtyvmR3Z4etrRkb820aN8FETfAtZ+2Kve2bOO2oKofWhhAk2u96SId0mm5tH2CMWErYMv8JITKGtKQUoVjP6jWPXWZHMaW1z1AIgaRqsoIhZzLCLc8poesZphIITFsN2hdXQ8SATrYB0lAYw9bNXd76+ptsWMvgPe1yWd45tF2HT5EhRdqhp+97ql5IDKvlimeffcHLly8Ld0GtM1D/6Pd+nws1cGNvnyq0/Ms/+eccHR5y9PxzXnv1NTm/seXi5VO2mhlb84ocVpjcUymPTT0OT4UXzniKDEnRtSucq+jbVpgxXhw2m2YiBT9JTGYIEVcpcioWBgqcMSzOLhiGHoWIiUKMRUGaJPSjaAxiEUIO3pMTNHZCzAnrBPqV301YXRJbFdJYZjl3IQWhQpLpfS/Hs8zWlNZF0SoWKNY5fPGc0tas8yhCFgZgLEaDv+zxr1XclVJ3cs7Py3//y8BPy7//78D/QSn1v0AGqm8Cf/Kv8pz9mlOOKEtHfmrhpI/0rZyuoJgwRJSR3+1MK9vXwQtnvMAwYwKMMYZQyP8UqXXfD8UATBNRBeMuXbtSa0/sFCU0xNqKlBW1q5lMZwwhil1CjGACKcnOQrxAskSX9R0+DOQcQMlFlvJAyp5EB8pjTNGjrlPNAaUxxqGUKYuSdFwpKYxV68DfFCKtX/L+J+/x2aefEPyAs5avv/01lNHUTU0aMjEP/ON//H/ji8+/EOsE4PbtW/ydv/O3ofiVDEPHj370L3n/Rz9iUoJ4X3/9EQ9ffR3vVwyDZzapyTEwqxvcfEo/RLZ2t7m4WLC7uyseGVrxzre+ydfeeVtM3WrH1vYmP333XerpBO0suzf2+Z3f/V2M0SVcwa2pYO9/9jGPHj1iIPHi9Ag7a9iYTtZ4+sePv+CiXzGoxMHZCc2Xn0mnn8Iaq37ywQuOF+fcMvc4ujjj4y8+p5k01LMpKIVzjudHLzm9vKCZzwk5c3pxwf2Hr1HX1ZrylnPm8bNnZGPY3NzkwSs73Lh5m4Tj5fEZG9WUrc0dVosVvuuJg2dxeUG7uKRtV0wmDdOpDJg3Nuc4Z4llNjAO0AQvlp0ooy8MowFeLoVIhuxS8KWpGIU7YkkRiTGTKr3WgGgEdsw5ybA5JblWU7GYNeJ1klSJeizmNp0fmG3O2bu5T51EOCch7rLQrQU6Wt5TTplJtIJvp8TZ2RmfffYZR0fHUAaEXdfRdh02LSH0/PSHf45Vkf3dLT7/5CN++qN/iXOy8/qD//Q/5u2vfZ1HDx7ippvMpo6Xh894842HTGsrqUrKYrQiqoph6BkKdKWQXIMUZQG1xlFXFb4PhALTGFvUpVrhnGWxOKfvWsk11uOuWSAZRuuSYikQY0TFUJo3CSZHjX4z4hDLNSjV+0G82ZSYCvZdV/QHnTB8rCLpjF4v1mNqltSCcZifkR2fHzx93yOB2b+8fP+rUCH/j8DfB/aVUk+A/wnw95VSv1Y+xefAf1eKUn5XKfUfAu8BAfjv/XVMmfJ7tG0xyB+EqhZToi//1kqGHpiSpl7YMsFH3KSWhSBdOUJKt5bJMYq8N0r01frnCjzT9SvGUIQRGjFGE6MuSjZ5GOVISeFsQ+UajHZY6zBVA8awuFzQdh1OJxSeFDy6WI+mGKRLz56MJwbPamgJQ0foW+LQkoOHGKBI0nOiGA+VIUuBaVARSYwSH26jFSTPZLOh3mzYvbVHbR2zyZTtnV26vsPVNYu25faduzjrePPRo+KkZ4RbPdtAq0OWlyvA8P3f+AFvv/4WtTY0kwlZwdn5BcpYmnnNkBKPHr3FW1/7BstuQBlLCJFnBy/YZgefEgdHh0ynM6DkwqbAJ19+xvHREfce3Od8ueD/8Y//IyhQWvSCS2Yt9qzT6ZTZzhY73Q2eHL6g6zpMucidtWQFN1+5x9bNfV556xGqCH100Sf0oWfQmXuvPeTm/bs8GjqGnFicnsguTusSRJ6Y72yjK0s9nfCzjz4UeM+ICtB7mde0XcvdB/eZTubc2LzJRrXFZ58/5Y/+8F/gqh+xMd9CK01TVVht2N7YIQ1Odiilu97d2eF3/v7vULkKrWxhcqh1YUhJrISt3HOspTBlLhSzmNhJ0RfjsXGBkIIiQR6K0WtmzfolM7qjCq9ba9ny56zXzwcSTGOtpSqLWoyZoCu0USRliOthZSQSMRiMsRKDacXuOefE/v177N69g3NuzcUevC+fo6fr+rI7VVxcLPj4o4/44rPPhUQRIn3X8emTJzx+8hQdMk0zJVcVTBv0xpwf/OZvgxL7W6Nk9z6UwacxlulkUiLyrgVzD21RdspxMlqtXV1fHhyU4X7CalG/G6VJKmGsFeFkRrIftMEZoTyThbEyQmviWyULjCqCM2tM8cChDHMttZPwa2sNSYsQUyONpQjVFNpYnImkEMvir9ZzpzEg3BZG4S96/KuwZf7rf8WX/7e/5Of/PeDf++ue9+cfWmnBsApDxlp7Bb/oXJz4rCQjxdEbkjIw8QxDj/cem2RrKR27knCJGCVxJiUJ+YB1rmRTNzgjN5t2VrLro2DoI+aek4QAkDUb8y1WveQzTiYTlFV0vpMg3BzJRbEo7y0S4kDwvXS+vqXrVrShJaWAzhGnFbWVi81ZcZtMGbS2cgEjxR3GdJ1Ezh4f5GdiGMBpmq0579z+NebVBIuhXXV8+NEP+do7bzMMJ7w4OGbiam7szvFeOo7jo1M+/+wJ2lhiNDx79hJrP2FYrmSYpDI+BpZty2uvvU43BD777CkvXl4w9GJnXE/q9Tnc2t3iQbzPxcWFbEGVpOFMNqbs3bnJG197i435nF//jd/AWbmJUkxMmkYUhgpiWXhjDOzcuoEPgdPTU/FQMaaoAiM+eC76ls39PTKZGWUG42ULvWpXOGPpYuDmvTvrhb+qapqmXhfGEAJVM+X7P/gB0+kU6+SWGIezIzUzp8z5v7zk9Pklf/Cf/Bnvvf8xy1Xg1777fd589R2Ojo7Z29thPp0ym8549ZVHbG5uip8QXDEokgzLQ5KdnXOlkCaxlFZJ5OeF+bpWRspuTgbqhTj0la9RZkNKSZj8iGtlRt0EmBLcYZRFpdJ8ICEfRmuss4ToqatKchNSJipLNoZIWMOjWlmK0QqqQD+9SZhqwtB3eBK2siyHQeiH2hCVHAMXGuqZLs2Z5/bmPrfuvspv/vZAU9WQhC3V28DybAFDoO88J8tLLlNgsrVHjyElhS3u90KBbdd0S6U0m5ubGG0LSUERJw0+BEJM4q2OeMMvF5c8fvwlzjnZ5Yzq91KkrZGa4L2XZmpkbRnxf0ohok0J7ignam0NUHxpxkAVoyV3wmpdKJa6UJ4FGnPWSbqVYv25yJnKVUzrCbPJTBau9cbg/3dsmf/MHrINhbppqOqOqqmvsgqVEgVXuahjlAxISdETJsrQezoGgUlCQmlbVkKJNzPa0PU9McsmWGlNHxOXqyWrtuXk5JwhCW4ec6DvW9rVJd3qktXlgovLlhAzt+6suP9wQh876ZzKkFe2fg06JklLz4EUk2Qg+oEYemIayClAjliJj8IoReMcTeWIPsjJNZroCwU0Cw1UeP0lFUqX8BBGla7lYrni/Y8+YlpN0RHZsirNbHOLrZ09Dk9OeXl0ikmRpuC81lU01ZTprS22t7bZ3d0jRcVsNkfNttnamNMHT1ULK2A238Bax5tvvk3tGmJMwliIxa5Ba3wI3L5zj/1bt1HFm2fwA2jNeduSVks+fvwlYfD0w0BVVSwuFwyDl+FriOTSVU/nMwk2BlbtiqZpRHIfI7pypLITE5w9rOcjwXuqqmYYepq6EaZUiiU0QgaycpMK3qxgjVdbbamtxSpDCpGmaaibhiEElNU8OTrl9M+XfPb4GYtOg53TbN3ik8dH/Oy99/ja197kb/3WD3j4ygOayuGjhGPWzhUqJ3TDIIM9ozFKr/2CRmk6xqyv09HeOaUIpgQjl/mLyiUYwsmAfHRCrM1ITEA6/SwWstZUGFNUmylds8IusFAGpSSZ1CiL9z1Z9qFCwyxBNSIzkQVBo9aU5D4HKmPACd88pgzWMaSAIaOcJRmDzY6sRFio3UTgU6uoTC389JyotMNNNfONXXTIojtwli5nVFURU6Z2rqQZZYyGGDzL5ULeJ4rZfEZVN3L8cmYym+NjZtm2jNV76AYOnj/n4PlzprVl6HoMVnab3qNTsRFK0lwqJWH3OcN0asm5hAkZR4oBU2jLV9AZsotXcu7HIp+T+Lub0kDEEgytVC5+RcWKpLSwOQuKIQVQfndsgn/Z41eiuPfDwBdPnzNpGtrLJS9Pz+mHyMXpGavLS3SWg1ApcV8cUsDHSCRjfeLg2QmVtZweX8hWWjvBvTPr1TTmREbRR49rasLjF7RnC4ahZ9FbtDX0xXI3eI8fBqIPDF2mC0tCipwuF/Q5YZqGZBzdkIWj3oOOpji8ZRIJMdD1hNwT0iCD1AQJTR0NWhmcNTjjUEluTuecqOmsQukI2ZeBW4kOy2KWpExNSBCToZ7t8L3v/SZWazY2NrHGklPC2orKCuvku7vfQ6GKv7i6YudoYRsNw8BZu2D75q5cbH7gYHnMcrmi7/pCEZXjl3JmGMTXRKEJ8Qp/9SkxZHG8C8giTLEwdtYxxEBSCqwCZ6mtJm1M0ZXFh8iyjSg7RVWWc6NIVoZPMSUuCu6sMwRrWZYOq7KO6D3bG5sSjBAiGLlRY3HY7IYepWF/f5vN2Yzke0wM0A/oGHCzmou4wg6Jpk/YNmAGCUTvgcs4cJk83cNHnHce8/Xb7FhDzPCjrsckhX7wGp+cdIS/+Jg2Tbhza4Pb+zcgeUHcEmirUVZx0a3oBk/jGrbChCEL9irGUUGKgfdsUDMxliEGsimQ49g9ElEqMfSt7DJLoe0Q0ZWgBnIDKBxZl2Si0sknEsoUjUkWDFkohIoQAxlhk1UpossuLJNL1GV53hIMYrVmK2pSH0BpBg9oSUgKhbwsMGdGaVlMKjdCClIMYxwj9QCnMNlKgE6tCQqy91TGYAqspqMs/M5ATiva9oy2uyjmYpnNsE3l5iXgJhFyRXITlI/UVjOtHMvVJZ+8+y4uRcJlj5MtM9pV+ChRgT4k4hBQWWG1lYx6koRtVJYcIwqL9wPD0NMNA1UlC1UkYbWI1XQQB0uLEhvv4HFa6JDD4FEWfJJzFmIook1N13frAa+P4v/ucsYqRfyb0LkPw8DnX3zOtG7w/cDh4Uva6RLf9kXJCNYodBSs0ZMlsUmBth3OGCrrOD8/F/pYlmRCXeZRJCk0elSR9i11bUl9IARP161IGnrv1ynzkqySqWqLqaYM0RODCCKm0xnLVfE7GVfPnNbK2RFGYcxvBMY0KFPUtvoaLTOEuFbOwpWdp9KypRzpc+WJCSFwdHTKRx9/QoiZlCVx3VoncE7OOCfe0QrF6AHva0Oyeg1RCH1MBGApC9astSYaUM7gnKhHXV3jXEXdTHCVPK90n5aqqtmcTCTcO0ScMjRVjVFaujulqeopq66j7XthCyjQzhByJpBQ1nB+cUF7ckprHW7SMOTEyvf0ccRSJe0+xYQdWjakMmFTxBrDzbpmb2eHnBL9qmWoHNtbW1hjeXH4gtOzE0L0LKtTLLCxsUGlDH4Y0LHllc05r7/xCpu2gc6jQhlkWssqBM67JccXFyy9x+dIUMUQQmkcGr9s2XAN9/ZusjVvaCqDShFhOCmc1ShjCL7n8vSEk8sLkrV0Qn4Eq8hamDEqJqoMd7b3ePX2XZpJg+q7AhXIZaCyeKvELJCjOJ5e3fDXQzrUta/B1eB2VP3mcv3IALnspLMihMwvsxAfm4RcZksSSK+wtpJGoOwQVAZVZPg5aZyr1uyhUGDVESYb84x1wf1HeOx69vH4GRTCGhl/31lLpS3TpmFSNzij1xg2CiZ1xazaJoeBoe/44rNP+ezzT1ExYhAX2Y2NyVq1LgwiObYjwYOyc44xopLF2AofEiokUoScCs9eCXwcUiIUGqSJmX7w6yLd+4RyBd5SFvKYyCz2BTGIyFHmd4rBR7JSEuARQknj+sWPX4nirpQq3HJDtlYUZBRKUEk1clbCGmIx84lKOkRtDPP5lPl0xtCJ22LKxQm9YFs5Ce6eFDglMWL7O1v4vieRuXlrt3SeUYZSSq1jsyrnaLtTLhaXsnDEgA++3DyiqhQ+sgR0jIOZ8W+uFWWNDGV0Zj3QWTMiynG4HhNYaA9QJuWCrkr33Ewadvf2aJop1UzUcigtBd4arKsLA0fENFkhMYXWlHnCVQCAscIQAhn4NNlQKytUPSNBE0ob6qbBOblxY0q4kKmVQVvBH4eYcHUjTCQf6IMHFL2zLA5fcLFckAZJzjLZyQ1kHSkmdnb22di/zaCEO3xyccbh2QmtD4Irh7SGD3aqhkd7u4LDlwGXMwZT8kKZNqTKMXWWyhpu3rrBanNWoJGIM5b9rW0a6wjDwMxYZspRdxqjooRTZ/FU71vPcLnkdl1z88ZduoklkIm6QCvWkkOkVpZGG3RIbM4ajOrJfsAqhVOaHAdSUlQKHty6xb27dwhGc5GFzqm0ofAeqdDQeiZYmlyheolR1ErgPK2FzjfaUdtyzY7eKutm4Nr99dVINrX+ujQYtlyLel3MR3+UNfPjFzwkAASyNVjjJFEqKeHtj4wzlamsBNr4AluMSVHX3+M4/NRayw54ZBRdW6iuvy5KQaFPkzPTyYSqmbC5uU1TOUwu714prBOv9fZyIfz7ywWffPwRKQRyEGhxGARGDUHozSEniAk3DpKThOegFM5WxJgZ+sCgMthIiEl+T6XiUOkZciB6j0NjYmKImawMISt8TKgyrFZZ4X2SmWJKks+bEjlkgs/krEX0GBPLTuin/8bGYf//eNR1zZtvvsGkrlktlqgY2ZhMubxYsKqWaKByDp3EF2KIomaLJbFpb2eb3Z1dhral61oqY4WCp4TqFWMEFD4FhiTMmds3djhfXIBS7O5toayRTqzw7UWyLCKQppE0FJ8Sfd/TTOYYI7YBwY8dfSArsX0VL5h0rdvOEo6rFNooTNZrawGxRPjqhbzmtedrHRisLWBBM51O+dpbbzHf2KLZnOJqUedVdYOpKlnd81UB0sZguoCOI33uqpsTLFf+n2KiSY6JqXGVUDF9kPBgqx0aUwaCkXmEJir8IBFooa4JynCyXPL09IijxQWeTGUdYRAhidGaHCOpDWQFVV1TOyeJT0mzkQ0J2FWWt3bv4upaYhSvsaE2Ks3t7RlG6yIfT1RGdAspRipbPLORiIqUE4MfRMBSfIgkLi8QrcVOJ5wOLR+8eMxi6FCVIxvxzz46POLi5IztzW12JjM2XU0fPF0YwChc5Ughsr+zy2v37mOdpjKpiIUEx9fFM1yVrstoSV1KCSZoYiiYu9bkZLEYrG2oMTSpYogDvXGSqmRknhS1QBlG6fWMKen01Z0ef/XQbdwVqhG6QRqNMXN0pFj+dUS3K5MuhdcwxIjVTvydjFtjyzF6Js5hlcXneGUroq4WmZF6uoZouPoc40IwdvXjdZuz3FdD7/GDzFoqY7FAGgaMq6mbyZpiS8ro2nH4/Dn/7Pd/n/d++hOyH/BdS04RoxUXw4qjl4ccHBzglCL5iEVmMtrIeVNaU7kJaM3Z5QrdVGAth8dnBOSaptz6beqFnpwVOkSWZ+c8fvqMkBK5khzapJXswkLEx9EATY5x6AZSP/Di6XPOjk45fPYZq+WieNb/8oX3V6K4W2O5eWOfpqq4sJajgwn7+3uoEWMqfs1VYTe0fqAPHp8irnJsbm6wtbXBfGNK5Qx1wftcufFjLLFbIRBIJK2EN41Mure25hJwrYqoIEioMEkm+koNzPKMl0dnnJ0cs7G5I3BGMSYTdWXpGotVKGUQCqkk2JctJgpn3ciBIKXwlRN1/Xxd+eFkkTMV+bQ1jtDLxWyMwSmDQ7Z3TgmNS2hlnnYY8FE+pwkZXQZ744JijES/TSdTtnd28DFxar3wpY3YqHoVREQRDcknmWukTMiiuPUx0IdALF33RbvibHEJWrGxtcUbswmv729Ll+nEjjWnjDVFJVg+q1PF9dMU+qfK685r5FeLWVyi0UWckyGXQbHK4tlvCgdcaYT9lBMqB1RKGGXLjS6Lt9bgFEy0ZXtjAwbLSiX6nMAqpntb3Lpzh1t7N5hlzSaiFg2IfbQxhhQDk6qmVhlDBB/ROmKdJQ0BHxNWyedWRos0vXDtM5kuBLBW5hHGEJQiKo3Pit5AzIaoZScWlSgyo1JkY4SzXiAJjcHl9JcK+ldyg8siMxZJoQfGssEsU1g0WV+DF3/u8fNFWTpt0WGQM88PDsghc+/mLbrlkuX5GTZDXVcEJUy46w3G9ecZr4X14HiEKK99f/wZqR0SdyifpShygfbigtj1tKuWs9NTFqeXnJ2ecnz0kvffe5cnj7/E9y2+a2mqihQDQ9cxW55zcnzMwcELam3JPgibLsqufnSa1aoSyG4YqOYz2uBZdB1og7GuLEqZLkvzZ1JC+Ujsi69+DFSzCdkKS9CWQa6P4VpDp4jDgElwenZGv2o5P79g6FZCEvibAcuAs+IlM5/NsUYzbSZcOIFonLGSmWqEXWBScYdMkZw11mnplGIgp4BkpxRoR4G1Bp8TQy6G/Rly8qQUJJg7i2/NKPmXRy7RY4mWTF2Jlevl5YKcYqGXpRJsDcqoQrMs2HvBMlOU1xQYprBctPD2U5Kp98g9HvnJIFRNa8oSkK6HHIsQYxyaGaMxGWwCZy0GjYoZm+D09IKLy0tMJYPUwSiUE/k/OVPVVTGpgqAjJ8cvODo64kTBouCxoUSVJaSzGrwMzeqqZjmFlQ2YrJjpCjMkptkwo+L+dItXbt/l5vYe89gzTWI9UFcVZImcI2cqXUKBU4TKMJSiDaKmHBU+Som83BpNlRVVNriqpl2thDHhEjlljChu1jhrSAGFUA4lvFwGY5I0Lxhq3Q7cXSZu2U389h4rlch1JepCH3EobFQS5qI8VWUxdkouWgOjapIP6BxQqTAjTCYpUUeLmVVZvAsTAi00XdU4gs4EBV++eMqlH/A5k7SW4PSqwilF40NJBCoXSNnpVUbos9s728zrhiqqdec9PsZra1zMQ4gy2FS5ND5+Tf0dH+OWP8awfi41LkilsI4deFbQ+QGVFe3lgufPD4h9YLOZcXZ8xE9/9EP+7t/+Wzx85QFOhXVe8TosvDRfwzCsoZkxnm78vLZkwI5fW+88EcfGyWSCM45p0/BPf+8/5cmXj1FZ8dOf/pR22WIp0FUMGAXBD3I9aU0MnpQiKcViTWFx1sruQ5Jx1q83vueqqrFVJXRoZ5k3Nc5aVHGP9L7UCHXFSDJGPGVG+wmttexMnRa4Uimh4l57LRUzseuZTibMJ1NU2qar3RWsdXT4C+vqr0hxF4631ZqBXPBf+VvlXC6wUsBzJAdP9AMxevF9CUFwvcqSoseVLTc6Uxm7nvTLawmuqMu2dNyiwrWt3siyKQ+tRLKtyHSrleD4xetDZ0AV2KRwVgUjj+tgADXWKCGprc3JQhjWn9+W9zmetK+8v9K1yJYbtBGRw8jpVeUa0uUPCWZVjdm7wd2bt9fdBrUTVWKBK23h1Ycg9qdt27LdTHl+ck6XZD5grKNuahkiJen467qhmUxQLqN1pjaOmW0wPqHaQB0VddZMTYVdabociS5hMtCtUFrjygKjY0ZFSbwhJBoj3OVcjrFWMlyLIYrCWCUUmgHwKRUWiMZTTgCyrR/hgi7ITsppizYylxiFQ7kstENtOAo9L8/PaJeJFtkyV5OpUCSVxqKZGMuOa9jZ3hJsNHhs5bBGMwQvhalcL8Y5YhQpP8YKRIYRm4oM2lhZPLqBNERs7djf2GbiB9rgScaQtC4+ShmdTMGv5c0nxLM+FdbQ1E8xsxnEv9y5Xx9Gig9TLqHxo1mfwzlbIJFMSqPaUu7D8fob1b+xWM2uB7YZJvWEy8sl5+cXbG/tsLe9w+MvHrM5m7G5scVsPhdKaXUFB47Xet/3a5hmfO/r7NHys+NrjsV13QjlTOUc0+mUvZ0dCJmTg0O6xSX3797ltbt3hcYYCjvMF7fPGMTqQcmgN4QgQSgPbrO9tU2tjTRJSuO0Ka6sFF8aLYPhpmH31g3sdELSiu0bNwQByOAHec5BiXePzZkaRew9lRHB2Kuvv4abNRLRlyCGVJoO1upopzR+JeE72/MNLk5rLi8X4oSpNXz0wS+sq78SxX1N+gdyjDhraZqmeJUkklI4rcnJY62hbhx9HFBxVN3JAEIrGVReRWKNQbpCWBWBkPxOCJKvqJEUGrIiyNQKCn0rJ9YZjDHkMmyRSDUZml5tRVXZwo7fI0n3Lnzkwm8t297oI13XMQx9MRwzVEbgiTjGbpUBGer6UKlwZRmLvVzoPifZxaSIiYW2dW07m0OkqmsaLKmPVywEn8gpYKO8xoyKZmuLG/M9BiXH0RWzq5jKAqFH7nVm1yc2PeKHHyw+BjwWKnHf9Bm8SoRsMM5girHXdRaHUhLSoq3CZIUaEslLsXfFSM0YoVwqtDyvtbRayeJa5Pfe2JJO4zHGkkLCdwPOGipTMYSAQxSG49bdFIFLrg3eTRloWbUrgrHgDH3qWIWhJPwo6gCDnuBmEyxCAQRZWCtrSSUgxpZhWwyinUhaDL20NUTk5jdaUytDlRxKaWwW697BCmPLrOPeMkElBjOqGIWfLupnGfiLYVvG/ILCHgp9cPy/7PqKB1Iu1/u6sF5Zaa93s/Eqs3P899iIaK1L96Lw3cDB8xd86+1vMWum+Js9zx4/LsHQAmflbNfvA7imsv0q9DLGy42LyvXfkXtbrRswbQx7e3s01tH2S/b39qiU5tUHDwirjr5t0UE45F0nFt4xeFzlimgtMwwD8/mcvd09Js2E5fkFsRtoXEVTch+G4MVR0sjcqZ5O2L+xSzWbERTcfnBX3B5TLsLKgUCUMPEIDoj9wMGzGTFFbuztMN/ZQFkLSnjzsezMJRoQamPxq47V4oLdrW0qLd41o1/PL3v8ShT3wlcEknTjVjNpKqGPaREqhODJocc4u95CjcXSWHFT834ogg8JkjVa/DRiFFFIVmIeFlOSYqE0uZiBFTmLFFDU2tFxGHy58KRoxygQy7gpFPhWCn1EvHDUGnMXTqzAKiI3zzmxWi7pux5IaF3/lR4R14dJSpXCLvdh2cKuCGmJ1hY7G2P/Kqx25SYxbG1uMXFTXCP0xUXo1xmxozpufIRBQgZMHDBVjS4XztCtuLwUsZeskQJ31U0tDCVrQCeSCgwWUq3xKpJMxuckWoQocwGdNUOSr6kkVEzfe3pfMkITJB8Jg0dH2XVppahcRR8GFu1KkqgU9MGvYYZcdiFDPxBSZDKZiN/QYsn9G7d45e5dKmXQKUsHh3j6W10oga1nOyt2Z/ukCSSVUcYRyHz+5AlHF2eEnNDOcWQDi48/RoXARl0zdZa9jTn729s4JwWelEgoUhYMfciZs9UKnyAVQVBViyrXh4GmqtCxZ8M2ZJWxKWNDoMrCsAoqsczFzK4UunG8o3pP5dTah4lfcC2NxTPGKD5NaWRpCTY8KrblOhsvjCuGyvXufbxex27baIOPicvzBU4bdrZ26Lue3b092tWK09Mjeh+oJ0JdJH4Vtx+71OvvV+iBV695fV6wvj8QmMto0Y0sL5cMbct0MiE0LbtbW2yWyMDcF+gpC/c8OQOaAk0KJFyVYBZbuvOkShD3+D6KClVpTV07qtpROSN0aaVoaocvTZhYG2Rs1mSjqVDYJI1YbQ0+RiqrqK0lGVFdG1PMCrURamVKaJ3llOqE0lksLpQmRE/ibwBbJmfhdEZt6LpO1GFlZTbaQoh0bQfJo7xn5XvavthwokSWnmXAmVIi6oxOmSF6gg+ojDBlvCdkoTj5IEOkGAPDMKBs8TbRctLJwo33g0dlsfolZ1bLpUiOy3A0F4w9pUgiyN95ZHZQAiTEMEwpuRhzUUuuqV+lC5Kb5asMhxFeGLt5ayxZGYxROIT6yLTCOCvF3Tq58VPGVQVbVpBj5uPDp5ysFldFsbwHSVRPTKdTYoy8vn2T29Uu2lgW7UDfeyqfCkQzLc53jrZSdCaSiGuhxcXpgsXlkspVgML3A5hK6F9R7Jm1LW6FyE4lKZkXhMqQKouxirqyNE4sKJrGkbCsih3rRBnqQYQ1YxcKopfQxlBVFYMfCJcrticbWOME+lGZHMNaeDPSTJPWhZ8cC+tGk7JiVte8cusWN/b2yFaTnSUAyXtUjNQolB+oq7rYXehiaSyzoVSosD5ELi+XnC4WDCHhk6Q0MXGcm4BOGRsjb9x/hf3ZBpPRjVQnrEh/qKJwztVYA1MmhOJrEiI5RvFBKZjuOFdBqbWzZcrCok5Zgjw0MqBFK5IamVpCy1NFK5LL8F3DerDNyKxKhRKSE9knjg+PuHXzFjlH6rrCD57pfMbu/j7nywuaeQ3pCmtXauSv5/U5VOV1rnf2V1j7VZDNaKkQh4AxlsXiEp1GmwCBXsV7SXa03g/kLBRXrRVamWJlIgrmYRioKnEttcV8T7tUnDvFmVGXuaBzDmOLq6qSQBK0Fbg0jQuTzPr6fkCRxK89ZZKV3aJOUresNQSVqWyx1gjjgLvUizIPNLrYnY+dffBrBesvevxKFHe5viLOKHzfUTtLbSw6ZhhEJu60oS/FMiaNUo6sxOOhtOM0rgJAI4VYZPHI4BGLs4rQtbLdTxGlwQ89MQ5YZSUNSWuULfSuMBDpMIa1B8rioif2AedqfC5eISqJ90YaRJuaAynJyppyJJPWxVu6dUmCbyY1k8lEdhzBQxKj/xzFgc5WM0aXypSivD9jqWuHtVXB3R3NtMFVjqqqMMatX2tSktZNuUEfzbZ4Y2NXIIG6vqKjKQkJHhcjY0CFFSoZdmaOeb1DlzPBGBbec7pYsFwtYGnIGAYSbfS0YWCxvGQ+mXJ3c5fNZsqGMkytLsPvRM7gbEVV14RizWrGeUMOhOQJJQXJGYvKGb8UU7kpityv6HLkMokRlXMSHOx74ShbbQSfVYoNZ9BVFi8VLSwQ5YrPfhZIQmeNSgpyxBhFTAMRJEs1dswNNBnqypWBuPjfgCtBzDPxLgIiShTEyI6QYv+6U1Vs375L2A/rLnr0tR/8cNUBGwOdLFCiHTDEokgVA8a8pjqGHDFOjKaiEmaPmHmVgqmkS0yIsCkDSSlU09DlRKyUQJbJEqNCowWC0BXKR1I3MKksWUXROiglOg5rGIp1rddRKJzZw0rj2547t/evHFKtZroxZ7Y75/nJF2zfckzVTYwSgZCztuysufKDKqljCktKmWFIcg8YU3a+MhMYm6AULSlb6skcaxQvTz6lz4FsoO2XrJZndKsVuqpZ+pUEnKiyI9GK0aglEzFOE2wmEOnbJaqPhEJE6PwSMkyjRA0OCaIVIVsaBqqZox96aXysYxiEJaNSJJPxOtMTRXVtDLHLaBzKVKSStJVilEUpK3yOBUKUt1nZGpLCOANOobPBx78BnTvIVmcIYgBmrRgT+SSDJlvUiMH3oBQmGbRO60gqUXKaq6l+0lercikoIttmXUh04VvnjIQraF1c7SRSLq3Ve+LrbLQtHV1i8H1Rakr/eTUcGgvYOMIc4SaZeI7ufdZYKutoiodO11291s9TwoA1rjwqCrXW60zHnMUvpa7rgoPGgttfqfqkwzVMJ1PhvF/r1teLTtlOCqYrxaaywhZISbqhIQYuLy64vLggaMWkmqBVFkvTgm3P6x1m9YSp0dC1DEESg3zfEny81q0p+n4ghnTF7FAZo0XVaq1d51uqco6Mk10JSmGMYM1VVaEyRGVQzVToY4Xp4YgYQeqFeleOl0KNli7jpHvNSBof62N/TUQDcnzX+HQeWeLXXUWNaAbyFS48/v5I/1v/voKJnVyjJV7RdsffWZ9Drobr1wU9X/1agS1Khy2zBSVWHTmzXK04eHkkuzmrsZW7lkxkUQlsVtzZv81GMxEXwiTQImb8LAmdRD+gskEngSs+efKMrb0bJC02HJU21ChubG+y2W/z2dETXv7oXe58Z5uqqoVp04s/UohxzVLTarRJuMLgrw9avzofkOM9DJ7ZbELXXhJDXNcCrRSmEA9679cmbtchnjG4BeQa8MNQICqN1mXuQC5wycjF16QUGPqBFCKurkhhZMbByIYhWQgan0QBPl5ddVWROjE6jCGiK7MWqMUgQskYItpcBfiMM47xOhnP/S97/GoU97J9HIcIk+lECr33WGexWrpqmy0qRoZoyAzlQ8rdmYqMPudMbatSZBVKGdYK0ixeMMYYMexnvAFFZpxTxocRY0dSk8atqpLBbM6Zoe+YTmcFRx/x/wLNpEBGtlaQ1l4aelxglEIDzrqCuXpC8FznE69pVyNzoPDUx4vx+oB1HKxeLxDjc1wfRCklaIE28tnSuBCRRVhlTCkqJbM2K2KQOUJKkVW74vD8gqPFgoFE1BrbBypthV5oNdbKTMMOAyYrKm0Ldz3RVA43m5GzbD2tdaUwKppmKsyfnMtW1a6zMGUoXexWi29OHwbpbIoNcIoR3w9rRWTOlO4v4sogMoV4BeEUeGKszFpLB/9XqSCv49V/+bL9KpPk+vU8rhNj4R2hsPFGFYEba6OpsaBfF7KtYyfzSBH46mvDFasCvkqnlWMhhIJKG9FHWMvQC8MnpkAGhhgYfBC9xRDofGQ1W1IVDr4q94VVRmCAWBZbpaXIa8UQEk9fHPLmO2+SrAhzVIiYfkV/dMz04pyHGA6ev+DT2Se89ugR8/mc61kLarymx+M+DmuvnYe/XOhZf+6qqjh6eXl13JQuEn2xN1DKrG+xNWUi5wJxBIauJ0zFwE4hkErsA13b4WaTYn8hrxtjRFkloqji5pq0QITF3q0I1cSSeEhC/R2DWa7uy7See40QXk6lVsVIBPEZSoUuqsL696/f87/o8StR3DPFRyHI1Fq5whqxhslsBinjFagQ193vVcsFZMHKra1QZPqux3s5EFkVLNZddayqbFMpw1DvPSrIFjmmSMqh2IMKnUkERIaLiwV9Hzg7P2Eynwp7JifSGBKR41XRLH8EK5cTPV5dY2eayWv2yMh7Ha/b68pVpTTayK5DhmEytBWM3X7FIU6KiF13yNKVqGsduiyIpbZAoZzaImbKOYuqcixKUW6qjY0NdF2zvbeLqmTYWAVwWQacyhlsXQk2mcXgSyexsIXxuFxZN9d1TQjC+XauJBZFwSutlg43i/WnHJDCHFJAZTQOcZ11iCkcOSJmccVgTI0UT4VSwlsG4W+vO7UrCHddXMfvjTfQSKu83sFfL+pfYXqtceJMyl/tOq932GvNAl8tYtcL/1c6/Cxio+vv4cr87WrgOHr/x4JbjEWyGMPTuJo7t2+XOMJOrJ6zeMVnpcQK2gsvvws9zjhSLhx9JV5NMcTCtzeiJEkZHTXO1Lh6SjIVQUXi6ozh6CXhyVNm3rN48pRaG758/CU7e3tMJhOZEVCGteP1xtVCNR7T64PU68c95yyuo8jxWK1WAlNFMbYLMZRjKI6lucBfI9Po+mIrymUZ3scEzjjQvhy/K9+bShlMgmgzyjoqV2GNJWrNWFT8IJCh97HQeuX1tC0LeQhF7JjFCC+Jm6xQLK+Gx0IFL97wCMPKVRW2fP6RHvqLHr8SxT2GwNnJKSrD4mKBdxWHWXN2eoZRZk0ryl4+eOsHhuhJKnN5uWKxuCT0PWdn5+SUGPrx5Ekn2XU9WYvYQdgmiZPjEzoCl8sldYFYhjAW9yTdd3l/RluskQ5jNpcoN+87QhyI18yXxhMzdvLjwFfJiH39eY2SrWCI/lpHN97MV51KCAFjE1Vl1lv6lMRLw1WNeMasIaGrAZTWsliYNVQlKTBjx6ILf7m8weJZn8gxXSXyID+SciJm0NkwbWqqnBlSpEIzbywuZYZiPKZDIEcR8hgtalkN6wgypZTYwpLRIWCiZMRao0RdmhVRCx5O2d6OPG+VxVNHa12gsLimqYrxWaSy1brDzUoGUpWWYAWNWuO6xozpVpmk1Rq/HW8q4CuF+/qx/8tahK8Wb6Ak9qS/9L2vFmJZvGKO6/CU8Xu/6M/4+tc79etiJWPlXFNCT4xK5GKgl5GBsVGaoeuonJFjlaXIJJUxgK2rNU8bawiFg520UFOjEmdDV66TnCACE+MY2oEQM8Ykkl8xLI8w4YxV2zPdnnHeBW7cuMFkMpFzVDqMGMQ/aISqxu9dh7quK1qB9fEbIRwQxlQYenQZbOfCAkopikVISqWJSetCX1UVTV1L91/XTCcTOn+10Lu6JjsDJfrOZCUEC6VEjFU810ffKJQmx5H/r0lexHo5JtCmkETkOMZCMEhKl3mfWe/SrC5NThlay32r1rsHrceS/4sfvxLFXStFYx0kpDAoRW0dlXOsY6tDJBmxqBUDfI3K0s0455hO50wnM/q+Kx4S0m2NopukxuAPOajb29ssQ481lt3dPQC6vhWWRQqEKAULsoQToNjb2+fRm49omobBB7TO9L0vi49HW5nMhxiKZzbloi1DXgF91x3heLMKRv6XU1XWId3XbuKcBMO2hf4ZQkQZ9ZeKzuhDI52n3BjOmeItLV47MRYICiNWDCrL92PGWMsQpRsWJZ8UV5MSNaW79IO4XGahFjoj/PeRbWSyODYOJZSELHOTuqqLw6OEYuvS3Q8psvRXcINQUuUGH4eWZPEnj3EQdktViTXzMEgnWW7YzfkGU+cIMeBMdYVdjx1iKR6uwBrXC+tftd0dqYJfCZOBNRRoikXGeD5HGuF1z6BRgXn9eYy74n2PX19DMeOCwhUc9/P469o3pfxbl5mSHwYyihA9pmgo5LNHMa4rx1+Pi5QW1oZPkfPFOY+fPCUpzWDdWvnbuBqdErOq4u6tW0zrWvzMtaLRmlxUtN579rbnzNUuh+dPUcZxcNZxbjWvvPKQuh4DXgowp8rCm3OByTRGXUFkPz9/WM9GMhht2NzawhjFdDrj4uyUmNJ6BuV9wBrLUNTDIXqC92I6pzSUDIYcRFtROccyRvpVK4PlEElBoytb4D5DhdhskMW4bRgGbDPB94PMhUq9Gs+5otCvQ6BC09QNftmJX1bZqWZT8iGKR70EDCWZGSXJeu26nsaBKVbNk6b6pXX1V6K4G23YnMxIMTFvJkzqWv6uamLMEMSZLVoDaALyxmPZV49wp7EWEyxJiShpNGVMuUy1R3feLPLgoR/ou4HL8wU+elZtS0heeORhIMZQcl3lhnrw4D45Jdp2iXWOqjIo2zAMdv2cYm2gIOt1pxmiQDvOVRR7dtp2hdJweXnJMPRsbMy/0pFprddqOEo3H2PAVlUZel4dvysa5Ve3rGMh896Lzao1Rf5eukooLAwKTjx2CaN9g0Aho9mWLt8fudYFwECRZPuYEqZw/0c6Xh7x7yQF1JbtL1l2CTmPOobA6fkZXxy/xHvPdDotmoa8Hlq7yjEONa0tFLW+Xw/CchKBzkwpKjKVVtgsw/oxNWf9yAXYyzJAX3/52rG73o3/VbDKuHiO2+Mx3D2VSe31jvs6Jr+GAfRXn/N6V/+V1173pnzlOX5+wSJLga8qyYANJfA961QYPKmgclciJY0qzqmiprZKM59MuXljn6w1C6WlUMVIbQzZ+7VHE0Y0ItF76loTgiy4tVWobkD1LbXTnKxWfH654jxP2CkpZd57KlWV93KN8qgKBSHH9XG6Pme6TjiQaz8zqRvOz8+YTic0dbMuqn0RCbZdW/D3q7nGKJAcZxa+0K/TWOyz4OQpJ5SyRJ+xxmJKSIgkKQmdse0GuScKVNL3vSz4WRw7Q4qY8rME8QEyBYIJ3pNUQkV5PYGDxVrclDQ6xmsoRoLKhEF2/PlvgrfM0Pd8+fEn5ATPvnhM0zRcHJ/zxedfCnMlCTablYiSQpLQr1RW/i+//JKjqub48FAu4GCuVnvKiqjHXEr5urWONgwMfpDQ4pzo+o6+79bhwyMPSasa6yz7+/tUlaPtloQ4SJpRM2M2mzGbzRmGgbZdcXl5SRgksDeXKDIFBJ/oh4HVxRmTpkGbkqlYPF5+fnC0xhrzOIS5KhJXDJpwhSMmGSI7J7/rvV937mPhvpoHjEdfUa6g9ddFYFvUiFL5xfWybD8VWVKElHQwUi/yuqBTuEKFJyQ86ihDTGfsOtotZeFqZwRG2NvdpdnaLIVcjJmk9ggWW9cCQ3WDxxRO//iGYzF7G+MZc0r4GFApgDGyqGiNK9toVEZjMEWZ/JVO+VrBHbHN9azm6sDJ0bsGFaw7y5RBXe0Ifv55x98zyqwxZrg+aMtf+f8I7/384/qCs/65slsdP4/Rskyv7QPI6xmQKosuZeuvy3U1axrs/g2y0ew5MdUiZdnlBI9vexpn1vRdrSPZBYbUi3cLiXh2wSc//CH9ySEnvcJPbuObbT744AO+9a1vsb29XSBqCbI34+cd4cmsv7JTGY/L9cUVKBkEMoT3g3jfLJeXxNUKV3D4oe/xQ1g3KTlfsWJ8WWzatqVpavTBAceHhywuLlBedlFBZQKZpmnoqwZ8JDuwTcPW7i4Hp8dUszmrrkUZK8U9lnlR9nRDT7Yy9E3dwJPHjzl/eYJ1junFFqkWLU+MEb9eUASz0Flsyg8PDskxMqkVp6cnpCQpZL/s8StR3LXSVEqUhpVxOGVpXMXGZApZFdlyZuWjpJgoROFYOONS46VA+CF95TZQSss2puDLI/E/lSFGCpG2bWXFHXp6PxRu+ngTQiKxubnJZDIt+LlYEcScUMXhrqoczkng9M72Hl27ol0tWS4u6fuB5eUl7WpFjJHNWc3Gxgb90JVt+18euMkNe7VArbu7eOXvMcItotC9sjYeu6CxKFnrZMhXmCcSqTYWHNYUQWk5CwYbk/jnaAWFZWMo29hhAKVI1pGMWfOoy4hbOnbWqD6gyMqQdSYq2XUoJd4wPkrxra2lzuN8w0qgOWL8FYswxGXZCYQs7Z1O8tyjB5hSSvDODDrL8QxkaqNIgB8GnLHrra7QLIuJWXn8/PZ/TYdNSXIGfkGB/3kWx/j/65j4dRwZ+Erhur6gjwVnvRBwhUX//Ot+9e9it1G6PKNMMdwSq4VcogW1VmAhhbQu6jKMloE3RuO0WD7YWHxfcqapZDdsgSpnaqvJOPqYaFWHs1OxNBgCVetpLjqqHlrvuHv/VSbNDp9+/hO2t7eZzWZrz6LrtgaUnVZlr2ij4zkYF9jr58koyzD0awimbmratmVxcozNkYvFAp0zy0uJahwXDO/9V5S2gnUr6rqmrmo2NuboQaiNVJZkBO6bGocOmWgzpq7Z2trisu/Y3ttjZ28fXZTSMURSAqMSIQWSKQHY3UB/a8HU1ty7d5fp7hahEkXqSMGOMnAiDF5sONCEtiOHxKSGbnW53qH9ssevRHEP3vPyxSFKac5PzmjqmtANnBydyMpZ1KQDpdvLScJ6Bcbl9OycrlqxXKyARAqst9qjT3Ysq2EofzvrRB6fkkSy5bROdBkLaqlUhOjXxkqhDGZQqmyrAsFEnLNr1s50WjNtJsSNLfrtjvOzM/wQ8ENka3vG7tZ0zVoR7DtijbhOxjjSoq7yKcc2exz8if1AXxYuu1bzaiVqxhB82XLawmcX7rvYzl4l04xQTGnOgfG4jYwTXTpvCr8clLrK3ERxZZNMUTKOQ8UCzwitskAA1zju5TugLWhTaF8aHSMxBWpdyzHyoqTsfM/52Tlt37HselJhwIgZlNDZzLXOTP4VqBzcv3uPrfnG+vVjzqiYin96Jock9L3yGWSuYdYDaMb3po1QdPJIpZRdYS6/N15jMSdxpiwQlNKq7IquUyQL3KbGQAvEltkIy6ccMEb8aL0pYjxZY+G//q9rj/L74zU0smKkUOtybtOakaQYoR2B4YTJEkoYjbyILWEUCgU+XC3eMRBNoK401hpsD3VWPNi/TW5muMESb9wjpYb5bM7zZ8/Z39sX/yjnigJbirzSCpSI75T6Kkvp53MIcs7CigmB5XIJSjGfzfnGO9/ky48/4M7OLsvjE3II1E6iHlOKa5gupyx5u37AB89kMqF2bm05MKZEWdVgTIUu18ZIc1QpkUIUM7IoIsPgA6u2FRaUsvjs5fow5d4aAqvLS1bLS5bLJdFpBg3ZB7kWEUO4EIJAjSFiEjx9/JjkI9ubNYcHh3g/iIDzlzx+JYq7sBlqoclRkaMleENKRqKrcrG4VF4GcOViyxkwYJLQ3bIx9H3EXaccFzxxvIFMBGNLNNoQsEZD0oUVosoOtaTBq4KLqcD2ziZKQc6W6B0hyjZLUROD2IYaA9ZZYhQ1XcwZUzXs3bzHzv5dvA94P7BaXHB5cc7i8pIwdFgVqayIJnwKZKWAiMaTsiFGg9ISnBFjxjpNCD1VrTFWGAspKIwuXU6I2KbBqEgKbfHOAJNl6AjSgWkER9cJdFKoWChp5NI9OXo/oILGjmESKFSl6QcvopeS4OO9J1zrrkbmkFGKBiBGgtJ0UTEog6prVsawCj2p8+RuoMs9fRzIQYQyqhi3td7ThUCbI5t7e2xvbrGd5VgbowveLr7/4wJITvRdXz6txl+nmhXsHyTTs24cfZC0T12CElQQGmjuZHia2kiuLW3lSNEI20SJkhAjVNJA8RTRBhsUVleycOBB94TQ4ZqGpCbE1NAOwKxiZjImr6ichD1XTUUfE4PLeD3grKPqK4QhNKBzpNISNamUqBz7ILMrnTUnF+cs2hW6ruhCKHCWrBA6Kypv2PZTuUZLRJ9zElwthAPZ/RrbcK46coCqzAhyQoaGSuFVKpYHmUc3b/HBFweE7TtgK75YXFKpQM+Klxi2m8Cd23eZphXPnj/n8598zG6zyY3bt/BJFMXKiLlcSHHtkXRFENDr4fS4G9LaEBIoXXN+0JNiZF7X7Gzf4N2Xf0J4/pLDn71PlRPncRDKIZGsIWvwIWJdQwgw9J7VasWdxRnHx8e8ePaMUnwwFBFTaYyc3OhoZwntgrPLJafPnrJ/65ZoQ1ImeVmcQtl9VFYcQ33f8vyzT1muWurGMD3dYggRksf3PdPZjDHBTanM0HaE4AnLE6IPmPoGajmgvCfrvwEK1QzroGbhIVu5wUqn/pe2wVx1MTJv+Cod8Pr2eP076xVfWCgSn9dLEIi1awGTeDjmokouA0ataJpaZMWFP2+Noa4bSXop2F6MA65yTJpJkfMX9ogSNkxVuSKGyGgiKfd0l57or4zQMoXjOyrTuBLBjFSxlJJQ+wqVKqtMVhqdZcBnjKRKyedIyKxH4IrKXWMRjJa/ScRMycguShnLRCWiTkQrPvS9ksKjlISaSBZrgmtsjfX5vAYt5ZzpLASnWfaBJydHnC5XJGPxOYtDX4w0TcVUW6qsxb/fGOp6QjWpxCRMKZrplJ29XWzKqGGQm8a54jmf1h3XuKtKMUKK4nNT3mIqvGUxhZACjrX4EqqSFagc8UqhjMI7TbaSfFSlxHyAlKBKspiGQQqeswpSQulEINFmkXGqpNBKZgzSDStCSByfHPPRR5/zjbff4va9XRF9KVlcui5gKItrDgzZk6pJ8XjRMiMKvnT+Iq5T2ooTZY60Xcdy1ZKHgWXfEoqhTI6ZFJKorC1l4H3lcZQK+0qu+ZJYVTjb92/d4v7tO2uZPCWUe9xEzLf2SZxydrFiur3JrfsPsTO4OJpw9HJB0jUJy/72JkN7yfliweeffMJ8Y8pkc042Gl+GqEZrtBnhuysa6XXm2Bp7j0Iz7NuOlCKN0ezu7vLq6484/PRD8czRGhui3DMUO+HS3FgMldHUlWWznlBZS1OLLUgKnuh9CXkZWW/XBuJKFQgwUxmJz0yl+VRFO5KjDNqHoRdKapL0Nt/3hG4gTyJD10OWr3etDH/FmjiWZizTrlr6rmOjmtJ13Xpo+8sef21xV0o9AP4D4Fappf8w5/zvK6V2gf8z8CrwOfBfzTmfKqmi/z7wbwMr4L+Zc/6LX/oi+YqQL9ssX4r3tSHftcHTiCqMj5RHPmsZSJG/wiYpn+PqdwsPVmXx7JBw3ywugSXkd2SQkMRFT8RCib7rJK6tbKGttegsdMvzi5Vs8YYgW7y6YRQTxTQQfJQUo6ETuuSIo6fROTIXZkSBANQVE2LMxxyHq6rQMzNiE+pTpHBC0NpKxmxOa4/3WIJNhujRxqEqSxeF5YJWKGvRVjQFwQeiDmgrrJmMCC9qo8vSl8hOQgzGY3tdMffzxd34jMuRTeV4fecGfhuMq6S45gA5UltDpSqsqggkej+gnaGua/zQo7LkcNahpwuBUBZOlfWa7SvWzrrAEUhMYgwoJwNroyQEnVLEx7lGTJEBuc7atkUZQ91MyUDrO1TSBd+sqQtP36ckNsilOKYxRMVotNVYI9F1Oaj1RauNAWWIUREDHB0vWSwUbV+TBo/NkeTh8PCCPiiyuyCZgKpBVQsmdcXtm3t0IZZ9viQRhRBQxeNfa83dew/YT5EhJ4YQMZWYuFFglZgT/aQwzQqLRCILDX3XybwlS0Re1QdyCGxvbNAYRw6STGSdmHCFJPoCD9y+9wrPXxxxd/8GWm+yjIbcbOF1IOkapS0Hjz9nUlf/H+r+LFa3NL3vw37vsIZv3sOZT1VXVc/dJEU2FUrRRBtiAidIBEEBEiQXzmREubARGPBFHF0FMAz4IlGQKwMKfBEHChwjcizBkJNotGxRpESySXazu6u6urqqzqlTZ9jDN6/hnXLxvGvtfYrdTdoWguYHHNSpffbw7e9b63mf5//8B47Kk/qGj3/wA7749a9iJrUEnqDzwSI02NvF/LN7qRgjNsmkcdxtJVe0bXjjzjmP3nyDb//WP+XyeIS+w7iY10meiB+hV60bMY2LcFAWVSiurq64uriEHLZjILO8pKkSEkQNWnPc7tis17KDiuCczzBWEu8cJSZ5RCeJbTHQHRq6Y0t7PFLXU9r9ERc6QvB0XSeK2b6jLotRXNjmveBuv6NpjmMT95Mef5jO3QP/Rkrpt5RSC+A3lVJ/B/ifA38vpfTvKKX+TeDfBP63wH8X+FL+8yeBfzf/98c+8j40LxYhkbG3caH4+x+5QSNkZsRrxT29XvzlY8PFQYZXBF80KPDC8bZKk3SUcTelHBQSUaKHzCrULPYwlhSh7frxZ9b1BOe68WQ1xlDXEzG3MrlYayloIYoCNgSRSJMxaRg6dMGDseQwkVxI8nMf7QZCgCGcJGPs0jGkfDjk1yJCNJrWeWLvhMfuPT5K189ohxxwrhMYBjXy0aPz3Du/w3KxxFhDyBeh/gzHfuTj3yrupbEYL06LE1MBMg0kL5iiTp4yRZIy9Hj6zMHRSlHiMMqh+h6z61hMphS2YFeUgvW7Tuiy2hJiGvFblMKYhCXrCrxEMgo1NdwKb84vUj62Nvs915s1IYEpSw6HBoySrNfJhMz8l65PiSjrfHXC6WqFLrKvugoUDIplJD83BcjjvbEVk2nFyb1H6EdvsZ1OsbMZVy9f8uyTV/zwg2fU0xX1fIZXPd7vqdOBuiio6jmL2QRrSlIIeAfaSGau0GcLAnLIFaagqHJ4xEBE0JqCRO3FiRNTopQc8hZFsIXYywKU0E09BigG1o2xuL7HxZ7JtMZETe881tScLE/4zne+T9c2xEoxO7tHt6uIz/cEH8E7tlfP+dxbn6O+d8b7H33EsT1w58E9zu7fx2grXvy37lm512/u5s/SS23UIvHvWonT22qa3Yaz2RQ9nZLqmsY76iw+AnEAHQR7KQaB36IiDovQfOBpkthgZK3F4HejANf2ku0bIqF3EGK+loVKnFKCEHG+kwD6lDKBoxdX0RhxTYdrO5IXkVVIIQuzZAHunUchKXXGGNqmoTMtQ9N7azf/Ix9/YHFPKX0KfJr/vlNKfRd4DPxF4F/Mn/Z/Bf4hUtz/IvDvJ3lnfk0pdaKUepi/z498qIz1DfFjxtrBNTWHTmcexmtLpdycq9uFJHeRP4JZcPOz5OQPQZg3hdFgVObBy/IuISEeKRf4gZpUFiXOh7ykEuYG8ebwERWZQSlZxByPR3a7HXVdM5lMBS4Z/GditgLmBlMkTxUp6dyhD8o0wboZirrKrBHv8D5RlobkI8kGClthoh59L4zWmJQ560nju56r62sObSP7BaUlLCCHnBhjRI2rZPqpbEFhxaWz0tleNontwDAB3X5dh0P0dnHf4aE2FEWJ78UCtS4UykXafk+3X7Oc1CxnDyiiwnmPNjErC3v8cc/u+Qvc5RXTR28wefCIBjmcrtdbdocjURk6F3I0XU1CrAlWteVOUUJWsoZw49sx0O6M1qjekULk7qmkCLmUsFVFH4JAZEajy4KUPXhCL52YTmILGxGmic02EsE5yaBFjMS0tYQU8CHiY2K93fPs02fUL77PnYdfoe/2fHL5Q6rlnLe+8ognT16IQ+qslGvFl7y42PDsxZqf+doDgm+5Wl/QNAfefOOROJw6x3a353q9Fio20HqZ1MQh1VCUonfQ3lEXlUAgSYzZFtMZhdJEL6waY61kuzoPUcRyRim0LfAx8N733mW93fC5z73FpFpQJs2DkznPP36fkzfvoVPi+cUll5eXLB4eUN2evj2w3V7z9Z/7OT69eMnHz57zrd/9Hb70Mz/D2b172KoixfT7MPZRm3GbIZSAECSYJonnU4yB0Lc0xz0//yf+JF/66td577vf5f1vfgvfdxAcKipi6qW+xEgkgtZEDcE50UvofM/EAf9WmWWW7/UkmoBCaYZkNp0yKqCMqKyDFHhQRAZRks4iMoG3Qu8wKKqyxIdBEKdRRg6VGAPeC0FgUJbrbAH+z9VbRin1NvAN4NeB+7cK9nMEtgEp/E9ufdnT/LHXirtS6i8DfxlgUhgJMx4oiFpucOl8htFXIAibi8moWJTvJoHYhcM76ZBuP4YXwfuItXpUE8pDjMEwQOfk5yQxmlKknL+aKWC2YFLLIsaHOErMU1TZZvZGyWitxQfB0bbbDZvNmqIsqetKZPnZZc9agzJi1+v6gDKDK95NwR/e0CEOMEYJJhGxCigfscpgk8FGRa2LzOQJkAKmNOAl9MGGxP3Tc8q6yhRDM/LJB765Sg4IFNm9UuebKPSOKonwJSVFw40Hy+Aymd/bsZvPH5D3QSk5NF2PSgG/2/Dp+++xv3jBW4/uUz6qUfN7RC02tikGSIpXz5+xefoU1lueO8edekI4Oc+TRUKlSGHzdJVUDuHQKN+LY6QTttNAMRxeRzJ7yiZNlTQqRHFKtHacaCZFiUuBpBTLcoYOGm0VSXtUSBRW4KnYeQhiY0uC1giMp5XNplh5p6LEg3+xWHH3ZMkddWTlN3R+C+unvPn5rzF/eJfd0w948/HnuP/wLi4moj7hhz/8AJ8sLhqaLvL9H37E1cVLzs5OmVRFZl0ZTpZLnPd0zouC0jtU3tFs1mvQ0CUPiABJhURlC85OTnjj4aPR1bKyClwU1bhR4n9iTDa6E1bXkydP+cH7P6BICZRmu92S+pYnpwvojuzWG3oPhy5Sz1dsnz9jfTzwcr1m1zTsmyMf/sZv8P6TJ/zKv/Tf4QvLE1wQv6XBluG2/mP4EwZ1c4h56oWmPZJioJ6cYKuKe3fvUZUVX/rZP8aHv/DH+e63fpfnTz/GHXa8evZEbL9VEsdLpUhVpqPmP6QhnIfcWOVpL92Y+lmTTe6SqKpd73IympjgFYXcF1rJ9KeShIsE34g7bFFg0HjEPFDl+8f1vexqEHpvkZWvVVni3BDP+V8flhlu2DnwN4B/PaW0/cyolNSNo9cf6pFS+mvAXwM4mdZJW4PykqKOEiodWqOtHJ6CwQwWtdl/JDf0cCusd2jvx07+Zry7ia/LXYDV6MLIOK3AFIbo5Xuk4dfRIuyw1jKbzSmcY7c/ZEglDhNbLvJp+N1yB0v2u1YE7zgcWrbbQGULqqJAaWF8ENJ4EceY0IG8HNTj974RYNyYRTnXCzsnJsS7IWRWiMu/p8BKMSbZEyRZF5daM8my8piLtcJkm1gEdEjiK22DTC8pCGVQB7H4VUrRofCfmQ1vd+/D86x6KBXopmGuNMpp4vWezUcfYV9cMd3suXp+BWHO5KunBJXo+g6tInQBG6FWlt/9ve9xPL/gl7/6c6AEB79/9z737ikwlq73hKSwpSyNdYzo4MULqHeYfBMPMJfzHlSiNAWlNmDS6DWulRSrlBTJy/RWTDXaWLxzDDBOQqNsngaT7COM0oCRJbcWuqbgx5bL62t+73u/g9YnFMWE08UX6I4zjq0npTOadkpKlhRPmFQPWc4e0Poer0qKYkJKls3uKPayWKbTKc3xSGWnIp+fFCwXs/zeJowpspagIGk5zDrXExHxUPSe5MWGoSwKCFGCK6yVw1NrksmagOyvpHMG6Je+/BXuP3rIerPm8uOP+L1vf5vLF5+SfMfFs57Q96QojB57cU1Ssvc4fPyE6XzO+f37fO1n/xh/7nNvsTy/w+mdOzR9S1lVo4Pi7bzWoYO/rWb1SUJMQhJny8VizsNHj5hOZ8znK8kMiI43vv4z3Pv8O3Sba3avXvBr/+jv8+63voVKsvtSBrzJ+pcMk0hsp86k3QGSybWFSIqakTJ9i+BhjMEUssNwLu/WkOuD6MevUfrmC4e/pRizBbOEgQwqWXGL9PJzcz1L/GRc5g9V3JVSBVLY/3pK6T/KH34xwC1KqYfAEMP9CfDmrS9/I3/sJ3x/CCGfXHmkH7JKpWNP2T1v+Py8/NQqZ1beGAsZI1akpNfd9Ybuf9hyW2tJWqGMBpvHHSyawBC2kOKNRwgoqqrG2IKu8yM8M5geST6rHCivi1Vy4R3/G2nbI9GLGZmG7EefF1ySIpG35IwLCTHMkovdKDMeJGWl0WUpizql6VTEh370FTdacH4JNQnQx2xlnCcMboyQbAalfBZTaDK/WykpDErYGErJhaszY+L2qHz7NR8O0r3r0BoKbbEJLBFTKOrTJcf9mn/yj/8zplbz3/tv/0tsvePTZ59Qabi7WrEoS6aTUw7zwAd2Rrd3FLbG2EqwUgRnJwSMQphIccgMHcZqMdVSKo2isZSXzEor8R1KUd6lEFAZ/7daydI0W7Cu9ztetg3eO+pJTVlK1kAIAVsYdKlQGhZlwaS3KG0pTIlRkurV+4azO2e88WbgO9/5mE9fBX5wNcMUHlTL9fqa1RONMVO268jHhycsvn9BYTypveRwOGCrgh988EMKm9hvLvnZr32B5XzGtCrwfSd0XDU4Wiai7yUBSAmUqLXCRM+8N5SVISWZKnQyaKfoW492gaLQVErhxqZCvmdUIu23ZQFaUc9nPFoueHz3Hr/wi9/gg+9/lx/84L2sGq04HjtiH6hyh9tGaQgePn7Ez3/jGyxWJ5hSqKhFXlKO5AFu9ku3m4VRpa00qYTddkMkMZtP+cpXv8zdew9Yr3d0LmCLisLUHENHWVjunp8T7t3jbDHjw/e/T98cbgqRFiqvHhq1zA4zef8zluI03NFpDJ8fiA0BcXElSfB2UjoHYMdRHZ9UJjFoua9iEEU2CnwvdW+ECwGTWWFVNjhDDZDnf83intkv/x7w3ZTSX731T38L+J8B/07+79+89fF/TSn1HyCL1M1PwttB3NxOzk7p+57D8ch0MiPESD2pM7yRE3u0vSUyEvViAiaTCUVVjiIkG9U43ty29RxMmaqq4uTkhGN/4OT8jGo6EejCyPax7zu6thlf6EOvKEt5YVW+8ApbihIQP6o/BU+/xYAZTtg04Otx7Na1Eg90YsJgx+Itn59+5PeR5Skkn4TJouVwWdMQjWTGWmXQCN9Za43VkcInTPIYEr3yVNYQTUIJ1CiwwhjMrUUghvhiGgUYYaAErXBIYLk1kKLKAcs30Ndrk1Eu9p0KqNLy/Q8/gN7ztc9/gYktUfMp9mTB8sE9+sMOpxXKGqaTCfvnr3j+csNRW3Tbcry6Jh0T1XJCCoYQZYiNMWLzpKaHBVxy6GRE3p9FQcF7bGFy95cpd5mjHVTCWUXSkmyUV0CysFU3h7WKEZ1EObhuGrHW1QIHhBRBJerphLcfPObx5BQY9A6DwlJ8Rh4+vM/d+2/z3vsXfHwFvW9BB+7crdHaEfyOySyQ9IamXbNpD5guUNYV0UV2bsvp6ZJf+ZX/FvdOp1Q6gD9INqjvJbbQlJAESlNKYXWBVwhFEth7TZWn+oSQBIqihGJC7DX73hFUwGTMmRAwg19/WdJ0DaayYDSeBLZGR8fnvvI13vzSl0Bboq7wPmGjYmo0hYKNixybhul0CoUllSWqlBSw3veS0atyQtat+/dHFyfovNhuJwV37t3ly1/5Cs+efcp2t6eeLKjqJdpo6qrApkjyolL+zre/iwpQKktMIiDCSxE3uaHJN+NIC73lED1mM0SkqPsYcUH4+jFB7zppSKvJuBtU5JAYRWZ16fH/eye+PHK/D8QQEUkFL664rnfjPfaHefxhOvc/A/zLwLeUUr+dP/ZXkKL+Hyql/hXgI+B/lP/tbyM0yPcRKuT/4g/6AZPphF/8xV9kvV6jleHk9JTm2LJc7ijLEu+DhGhgCN7jQ6B3jrZt0EXByekJZVVRFII7lujR9tNme4CUEk3TMHiTP3z4kJ6ee/fvUU4rdGHBKLq+o2lE6Sqnr0iaBQbx+GzWY6wVhRpqLB63peUDLJPS0P3LIC8JTyqzOgpuWwWPVB4QL5YoND0bhaYn3bSMpoRE1ELde7K/oAlRPDq0Ydg1WSuYns43jCVSGs2De/epZxOUglIZAsIJj9bKcspIVUsKfH7OWmtx5VQQVCQqKf6D7eqAvQ/X3W13w2nU6KPn88s7PH/ylG/9g1+l9J5ZTBwvXhKfb3njdMWd6YoXVcnD+w+5PDh+8M3fpdcl55Mpzz94SmwC5arguD8ST1eiTu3Fz1vnBCelBINXI8tKY6zB9T2eiC0KYpJuvygLuYFA4uz0zZ7AKkXME52AroZVPeXB/JwQIj6JGKaoSjrXEUi0fYuPntPZKQYLUaC1FHMDQMSWJUlZZtWcP/0n7vNndcKFHZiWpDwhKlIwRA8kT11rXLD0/tHomliWluBblrOK2G3QVbZaiCHDTAIrJaJkcypFVIntYc+7771LnxLHsqKoynGJXxTFmIo0dJin8yU/u7w3WjSYwZcHUFZ2NT4GfEooU1MUksblXEcXwZRzVDKYBCpFYnQYa5hWEzlgjaELAjfEhIjBrLiBEl6HUYfr6/b9FXM+8CEfrl//+lc5Ng0Xl5c0jUepir72zBcnKDxFkoSoTz/6iN/89X+GDqIb8FEsRpT3hN6PUNDQqSvFaLWbf/pIQ5OOPStlQ8jOoJrQ5ekpW4IPrZsU+rwkGA+IvMDWwsn3PmScXmf0QKYUa8RwTGktmgr1Yw69/PjDsGX+C+DHHRW/8iM+PwH/6h/0fW8/Ykp0ytHT43RPFzu2zYbdcUflq1v2qAVRRbwKeOVIZcSlFkcHMRKNw0eHDZWIIJBUcR8lZqxPQnMyvuPV9ppds6fcV5R9SVkJ77rretqu5dg29F1P23c0XU9Ir3jny9I5N0m6dFIU3Ct5CD2JNjNhAjqHdlQqhxIr6Qx9iHRJMbpGSk5QtjS4haJpT4wFMQiyICuNKF0GCZUsWpcobXl8OiVmj3g9KAzzGFuWJVX2oFAhQopMqUhHL/7UpQUjft29TgQj1g5RDb7mMjoGhfiWkCi0wXnxfFFKo8l+M9lT3YdIUdbiTx0Vqja0voFqwv3FO7xav6Dwjscn53zn299h2cMdPcM1nsMcrNHM3nzEY+85PH1BCBp38Cx7w2oDS5fYtlfM5jMOYU9UlqqsCAqBm0hiBxEDWk1IIWWbOZG1O38Te5ZSxJgKlUp0YbKVQoLS4nzEKAgaiAptQFmPVhGdF+cm9syyt09tC6CgVGDKRAwuQ0cRjSU5RToqpoVBd3smOmDLAq81LpY0DspC4EJsInjQumA1ndDFI13XUtqC0lp815PanRxCTlOYKdpYUJ0UbKtRyYgNbxbYnNdz/uRXfhYXgmgctBr3OCm/545I4xyb/YFp0BTeCXSgFU0KqKKgdZ7N9sjZ2Rm9k4OV1OIKS4vCJzH4m+KxWhbJLuUwcw9JKbZtw/XVFl1YyqqCmHUMRqCTqUlC0VSD1iXmBaJCCGNSkkLSTKcT/syf/XMc9ke++e3vcNw3zOYL6sWUkzsr2Q/1E3rXUxrFZL7CjRoNmU4LbSjR+AQoS2Fr8C4niekMWJLb90ShC3xMFKbCKEtdzSnLieywtKWoBNpJmRMRfUQXBUbVKFWitEcpK01GIfkC2miIhuglBwGlSL1Dx0jqenRRUc5PsZO1MMrsHwH7gaZt+fa33+V4OPD0yQuWi4bdZsdh31BaQ4y5a7UyNg+iJR+EWeO9LMn2hz0xREqvRi/0UawyLGOI7PY7Lq+uaLqWvk+EGKUYaI33jt75HNqR8MHTdIJfBOepZrXE9TlHtk8kBZcXLPEW514EC6K2y4ZTQZJydEAWXrnY6gHXVzdwks488+F0FqhDfm9zy6GwMAXW1oIRW6EbanUTWViWkhYD0o0SxE3Ookg5KMBmNSBRCp7RZqRdqahIXr6XRWOV3KAxRFSIHHbXfPD99/F9Q980pBCYzRa8+dbbrE7P8SFhVYEFbFUwqxf87Ftf5eK777N5dk06RIyz4DSrPuIOR/CBUikmVvPe1Ut2uyMrGzmGI8e14zf+0T/i/YURXxJjuPfwIT/3Cz+P0oq+l4mhxBJVgVcKlyIupmzVEtnuD1yvr0kqB4MnQ9cGEeZk/L3IYRbz6YTT5YrSWFliWpvzeQdcXyal1wI8SGMwhrGaGBSBOGZiJi0HiEsB5zLl12TnUIbClUBZFJakDE55ok4kA9gckJynPqXEGqBPEasGxpYIq1LGdkOmGVezKRWge5cxXXEfjAmS1qjC0oXAcrmSwPUMOcghr3Ep8f5HH/Ls+QtWq5PsNSMLc2utLCbz9b+cz3j78RsspjN57TL3Xpsoy9uY6JqW4DxFUdAdjjRKc3ZyCkqNuarDLk3+/nrtUGgm9YzrqzXvvfd9rC34/Be+yHK5YrU6JSFJbNYqGcZDYDafcv/BfTY6UKpEc9iREqzu3IHJgjYETHuEIGwxq2/yEZSRe9OaAm0sdx4+4Ejk7qOHzFcr+R1tMfodFRMR0Kkoge/RixnfoWl48+3PsTxZZcw+EzlAro9hd5XplNcXF7THljun+bXJxmF//4N3f2xd/ako7n3vePr0JV3bs930qNSx3/W0radTOceRlG0A1VjgYxJvi65JhOQ4Hjwk6d50LpAh49fZQFAw6yQXTNtGqlpoNwqNMlo8bUIipuxW52W5G7wnpShUp06EBArxWI85q1HpGxqgOPSRxzdJJkJblDb0XY8LflzuFTlMQYaeG4443NzoA8UyogQ60TfjqgHhxLokI3J+DtZYTKZ03b5QVJC0JvmZDjJ0JVYGyESgzLjojUGWj1YbCivQl/IBekeZFC+fPOH5Jx+TnAg0CmN475u/iS0qtLX8sZ/5eb74xS9iKEixYVUb3r98Rugi01XNVbtmFw/88OkPuNxUdF1L17Zsrq54vnlKlWD1cMKxnnFEcbj+lODP2bktylrOFncpfIFPicosJAAjIFatVkFRjRbF2hhWVc387Iy6Fu9v1wUIAkclxFueFPF9T2UMtbYk78FHUvn7/dYHat7IEiLH/Rmbl2/yvpqyIEYkBEVpuq7lan3kcDiIxYJQSyhsIfuDEEVnMKnZIcrh0krBISZhuoTIarHg7OQMhby33gtzanCJbL1HaSPssLz4DxlANkokWQOMpYAUIqWRPNDeKPqUiApRDkfP/M4ZX39wj6qqKYsS3/XMIgJrpShZCN4zqUpmk+loK6yT7IqSD8yqmjfuP8DUJZNJDRHJbfU3MOXtpKsfJ7VPSdG2Pcdjx9e//rMsFktQRqCNEDnsDxhbYGhZr19ydB2rwvDW599mu6yZFAXb7QbnPZ976y2K01Oef/oc+3SCiZHFdEaZDeqMMRgrdaLQlsViyYM3HpPmNY/feZvZYiGMIGPp204sBwo5/PHiZ5RC5NX2Gns8srp3h+XpSb7nsp1IXrw6L+HZRimSC9imobKW2WLOfD6XNLaRzv2jHz8VxV2OKzHFQimMLuh9JAQyO0MWd0mJpe3QjcdMSfJeXhTvBdsOEdDCXIlISIcy8vEYb9JrYkjA0BkjBRrhj1otUEUCVN8SM6UuIPxgrZFlR3CE4IjOk8wNVUkBqCylVowuizpaipgFSiphtKKsihFbTJBpnLfTgRjlxnq0PZXC4r34aWttMUZRKJP/SVFog0XYQ9JtGKGMZYxQKS2pRjG+JmUeMEdtJPR6iOXw4cakTalEUUmX8ku/9Cf5B+trXnzyMYtJTVVYUnD0Tjzvf/Dhd/nk+mNSoSknFant2PYX3JvMmM0m1HcfcSwMv/7iY5x/wHQ+ZXp3xerNtzj94/8NUvTM6gnWZNJmmyicwFofPvmYNga2viNZi9JFxm+FzYOKGVZIjFx+a9HJEnNqkYqOgZVmrGggjAJVT7ApkXqBV4oRqtKv0fKGj43FCFkIitdIynF6ok71KpIKsY5ovCNNKgqr6I9HkheoL2olYVkxEUJCe/HZHSaEEMMYxBxTykpjSbtKLo1NApl/LUiDAW3wme8eFJBzfW+MqgzBeY67fRa2QWMyrKhl3+JjEDdHK5NGWcpUNlM2HyYpW12I4daAJRsrN2bCY4GiqiiCHDRTUwyXmCxUrZEFbl6m3g6juf0Q1ltBWU54+61zBgdZ5zzN4UgMkpRkgJcvnvDs4w+JbcP5bMb8ZMl+t6ZLUaAkrdmFwIKEQ/zbo0p4FSWhjJSFSUHU3t1BhG7XU9b7LWddSzAaKWGarm3pux5TyAQcey9mdSFxtV6zPx6YXi9wKYp4yotupveOfgj1zm9R8tK5901LOLR88sknOYDnj0JxB2ypocmMEgtKR7QV+GKgIQ0rsjhwkVRCm4RYdiisRKRiQt5XZNFBjDG/6GCNCIfkZoyonCiutPC5YfivUARTchSFoe87+r6h1BUxOAi5aw9OgiwyzXFY7iltsrxdWiSTl51D6Ig01C7vUPUo0oog/H71eiAzw3MaWD8mZq/uhHc91iJOegyfDyqZ/Dtk2pY1eC0dmM9iHzlUAjqmm5i4KLFgKiSsUlIk9WB3LONgTJHegY+K6uwOb3z5q3zlZ3+G5Byr+ZQqpwEZYwllTWcLehWZLGYk57nz5xaoxnG6XOJiIOhEF0qSnlGUBUVZkpQiRE/vHGVVorWibVuKuUfHlsvLS9Zxz1vvvIOfZtaLcry4WnO53mKTohx87LViMp2ibebzZ9ZUXdfZgkKKx3Q6oSwKSmvAeUyMJB9x0ecQ95uQ9ds+5MOOAzJkxeBzlPUKSuFiIGkN2nBojry8usIsF2InXVYsVjWL6Yx5PRF/785RKkNZlXRahD1WawlGz11g3/cSG2dtZrRYjCkkNi9GfIYk+5iI0clyNSX66NFpoMJCysvF/eHAR0+eihV28OhCXqs+OJkugLqWOMuvf+WrFOUEBi+mfO8My0ejhXliEGWrMgpTFBI3mKBEFo64MG4uVf697K2i/uO7dvBRMO4QYvZ/cuy2W1zvKGxBVZZcXl3y0fvfZX15gWsbLlFMyor5mRwITGd88uwTvvm9d1nNp+y3OzaXV+gYuTRDuHpuuLIln42yIj27d4/nF6+4vFpTTur8fDTeieIZI4cbQZamruv58MMfcmwadtsti+WSoJAcCi1e/C743ORqghex3PZ6TbM/0N054aOPPvqj4+dujGKxKthsO4o6oaxD24jNg6JWt1SqMq0Kdp0UpkhUE0XnPFpnJgsGM8hzsxdBUqIsVdl0fzKZ0HnHbHGrE84TgbYGY+VUDClwPDo613M87EELdts0DSH6cUmpM4nODHz1gZ9uLNPpTMbhmEaTJ4hiDZyjyca0JLL7XFZ9guwbrMop9CDKVK3H0Uyg5ID3siAsy1KYD94Ro3RAzkVC8thKwsC74KgKsVSISQuVK3kJdTCGoPIkE0Kmaipa14JrMW2W4GNxLmIIsFihKstbjx5wulzQtx2TyUSWl3EBZo4zIjoxSjPRBbaGPkSOsUeVJT7tKVKH8gFrxJkzxYTpeqyX6cL6SEgdTbfmxdMP+PLnHvLwwRl9vydqizUlZ4uKujonuogNahSL+BAkOCFGuq6j7zqa45EUE+3+iLWWL33pi5ICRcKFIP5CUbJOh5zbEfaydnTmHIr7aL8QxEZWF4V00FoSpwKK3vVc73Ycupb+qkdpxfF4ZD6ZYh89pjYFtbZERBVqypLaDxx9qFSksMJjJ4hls41gbYnrW/rsXfLxJ5+w2e3ovASimOzHo7XG9R0GxcN793l07wF910NUnJ6dsTw9ld4pQ0xy9slr4IKXKbbvmU2meQpFqMQAWvYNA5woC9s0CuTk/ItE3+fXK+K7BmMLYZ6olK/X1+Gvz3btw04JCkJK1EVB3zs26zWb9ZqqKJlUJevrC779O7/NxcVTfN9LZKAtKUrF/OwukcTi/gO+9o1f5Dvf+x4f/fZv43ZH+t2RSmu62OKVhIzndBdplHyePiKoPhCbLrNuFElJ4LVWCt95IlBqIxBpSBQBrIsUgfw+SxN1PAq91hozqvPxAZWg8IkQENjqNcj2xz9+Kor7ZFrzla99EaUT+92e09NTYnR5KWXHi4CYx9IBQ9YabQ2np6c0bcum3mC0ZmmXDIoyYy3GGvrgaZoGtGI2m3F2dkZ99YRHj+9RT2q0MXmnGLPniCykfAjsNj2XV9dCaQsu2yRIrmTKI7I1mmSy22C2w9HaMl8sqeoJfe9omiO7/Q7fHenbIyl6SqOwk5KsppafncdtGfk1eug+lRIbgCBvODAuja01QMQ5CQ4ZRCw3BkOJ3WFPs13LcnTkDw8iMdlhiO+OJkb1WtdkrCgzQ34exhjQtTy/FNDWsj0cefr8BdVkiilKuqQIylDFhtJ1pNIStBY7W29ITmAo7x1RFTjWdGqHDxq3V4TOoXyiORzFFjX7ccTec9hsmBSGr57dxfpI5yVfMhYGVc9wVtNGiEbCF3S2RxhYCSJ+KyTFKiSaYzZ7s4aQImVViv1xSiQnHW9A+N5DER8etwU23nu6KOZRxlpcFLO5qBTHrmV7OOARdeiDBw/QtRzEMUrHWxqLaxuKooIooc46RorcMKiYKGLChBu6p1FinqVSoomSoDWtau4/eMCDR4/xMdI6NwaLGGtxvTDM6qIipoQtCtpjhy4sOk+EAIWS5kcrT2FLZlWN6zpUUYndbi8eyE2ejpRW+Cwiq6wRkzZj6HqHNQabD5ckLAlhuUWBT3SmbQ7Y/2cft6mQ8h7I/WKMomsbNus1r16+wPc907t32a4v+eY3v8n11SV9J7kGs/mS+WxJXU8oy4r5csFytWAymzA9OeHl7/0eex9RveQqGCUWF8NknY2iiVHhk8K3smdKzlNOZ0KX9hLhZ8qSAnlfdPafCV2Pcjkztevp90eSVmhlqY3FK4m2TCiathN2n5Ls2ty9iff+rb3cj3v8VBR3lWXaxhRU9YTZdMFkOsuYmfA7iVKGvPe4IKeissKYWC1XGFtIaoqxTM00J+fonAEqogPvvRDi8s1pTYExBdZK6HTv3Zg+FIKE1TrvOR73ORJPlIp9cyQEJ5h5tpI1CvokXigqswKqapKDDWQU17ZA6YIQMqMmOIkVLApRjcLImiEzaIzRsm1XooIdYsmE052Xr2Q+vLUYq/HB5W5VOvmUOcIahWt7wUSLMmPrScKzi0JeZ0CVxc3HbJGnCDMMDvjcmdW2RkcRzhQqcdiuuXr1ko/f/R7T2QxtSmJMlO0LrLsmWJHkhz6gXMLtxf/xqjlyTAFlAOfFKc8FKiylKYRR4SOT6QylNccWVqf3OX94n0+//wRrlexelMZOF5w9eINqMkelRJsy7DYyl3IXhoIUSL3HOXl9JtOJUBO9p+klnUtpRcwKaEHt4nhjDRj7ZzupmAZGV0RZgUearuPi+pp92+BCGNkriSDc+yipXhoolGFSlHzu4SNWswXt8cCkHrx7xHckGpMbgJCfgxiSRcSXyTpHVdYUZYXznqoIuW3WlGVBSI6+69GIXF6RRVtaiVgpSb6tipGyLEBZfIpohNvnOseksChreP78go/W1xlW4cYbRivOTk5569FjDIyFPw76zkxxJMNvRgmzLGTe9019eL1zH9XPmbW2P+x4+fw5zeFAXZasljMuXz3n/e+/x/XlJd57qnrK6dk5JyfnTKcLZtM55aQW0ZKV6eTBo8e89bm3uHrxktVySei6bPebYdQkWoWQJI3KFCWmLLBVST2byvspngWCMhkjk7H3sk/RGlMUlJMKlxuipPL1Qi/CxJjonUyCZWVIBdS2pO+PpCSGigMs6P0fgbCOrnN88MNnfPrplSR895rrqz0xJLQSrFpof4JBuSCeyQOOWtqWY9tx2DuMibhwPXamLng5TZMIn1Cw2+3Y7XYc2z0JmznxirZrs/AgCT8+x10dD0fQ4H2Pc0ZCtKPHGjPmlzoX0KWRhRA5tSa/ASEiy15jqSYTCA1GRbpWhEfA7ysSRg9pM7m7zv82FHWV/00YNJmBkf1Beu+J9COsk6JYCczrCZNyglLiQqe1xnmPUsgSUcv0EqKEixgrrz2ZXpfioLJUNG2DpUW1Hdr3+L6j8D2n0fHk/Q941nsSWhwawzUh7qCwVPVMEp8c2KApqppgLOVkhkmGWVlRn9RMioq6qCisFCcXI8kYeu95tTtCNeHk819iOS0wBLQBl8ArQ5hO6ZTgl5WWpbMKsrNTSAeFYjQQMwn2TcvLVy8xhcUUlsVihgaCE1vi85NTJkWB8ty8F59Z9t2ecsh4stHCRDFFwf0H9zkN4s6IAmM0lZVi2+WgGq01oXfUtqBUBt81KGuJyopaVvpGiXzLfABj5VpxzqELK4tMbfLCPEBMYlanrBSE3lPUFowcKJ88ecrF5SUhJnRVYqqKlPHf5WRBUebkJddjtOJ0MWc+qUnZ3+feoweYO2dZcSm+QDEEVArUtpDko6SIeU+BFrhHpRubgZBtH5Sxoo3g9xf12zYEsrBFIMQrx3K14M03HuLalmdPn/Dd736b/XZLSoHVcs79x29xdn6HyWSO0paymso3VYmiUCgiIfT83C9+g/fee5errofCiDJ96JCVdO4mRXyCej6nXMzQh4r6ZEHSJjtDKnEItVao3MFLUE62YNi7FvYlZw/vc3p+JvebkevRR2ko0eIvQxT9xMWLl7THI2fLk9dstX/1kw9/bF39qSjubdvxve9+wPXVGqsN23XDfncQpgDDyJvZCul1P4fdrqdtZMu83+8BKIMfxF+y5oygLDiXQGeFXwh0zrG+PpAQnM9nfDomMpc55GxVMejyvaPVSGG3dnSLA+ngZUqUJaJkcCqapkWUzQpblGhjBSePFd61aKVHTuuQgSpwkpGDzRiBjBKjf87AtBm9XPJi1IdA6vtsLZzl8vkmjSmhg6ayRab/HSiKknLw5OHGu2ekT8YESpgzBimSBCuiiqaj228wzQFDYmINfddC33NmNau7DynKCUkbrvrHrAMkbZgvVthk4Rgw0eB85Ni39EmzdjtUkTDOoHqwJqKtJ2hNNCUUBUwNjpK7sxn71YpQJFbTEh86fEx4ZWgnBV4X2N4x6brsQmnQukSrWwZtmcWUMhxS1xUuBtquI0SJREzBU2jDfD6nMAaTl6g3fkV6XGyP7JkY8Vr48l3TCFPGGJkSc6hGTEm4/yliUqK2EjQeYp6mjHRxorfQdINDKknsd1PmfWtFqaSL76LHKpvZUBGjUrYL9pm5pSQ7Vg3OhpJINp/P0cbQOi8smsLiSCTv2fue2Amc6fpOptTSUFRW7KIVOBKT+UwaFQ1lX2ZbCMaoSqUVLjh8L8tSw427YlEUuL7PzzkTJj7bqcNrrzv5/nchT6UKmubIxcvnvHz5nKY5Yq3i7PQujx48oFrcwxYV1WQu1yXZXyoFXH+gKg1FUfP5L3+JP/aL3+AH732faVmgs7hKk8dzlfJCOnJ29w7n9+8zf3rOF776VWlUkpjxBecwxlKVVg73/PtG51F1yWa74Qtf+iJ37t2j6Tuc78TgLgi6oAZShQ9UwpbguN/zYHnGcb+Xg/zH2TLkx09FcY9JFKdBSVC0CxCTLBBj9leARDOM2Df7T4hKvt73QMycdulmhxEyqEAUmjIkKHVB56EncfQSqqGT0Ci9z4G2MWQZsHzt+b27zGZz+tAKPVCLuRQUKGVlY6+QC15bCfMgUQyLyRhQndChTGHoG7HUTSnmghOlsCcjObLREm0e8QbHSAXEkOESK0lDWmdVZu7+86JpSJpSmWEhAc6BPkhBQEMfe0pToLN1aEKgBF3ksO5sXSo3FESViMFjjGF5usKenojR1mAb3DpeffKcj9agtorJRCYZdEUygj/v9p6iVHiTCLqHQmFWM/rgSH5GPa2oioK7Z3dYzeboCJOyEv9rL5a22iRMQVZGJlwsMLZC+0AJmKAps7GbtwVudO/U6JhZSIBJQl3TwMwmpuen5PmbopTXOIZA07QoFGRVrizcb5qBm9E6oUyGzZKidY5nFy9Z77eoQqiDtS353INHnM2X9M7TxEQyCWMtMQpLItlEr7QIgkpLEwMzFygrCXMOLki3qTUpgHMyVXkX2feNUPFCS1lGYXooCXkfoamU0H1WaGqFXUyY1paFyhGHaEorUX7a9BAta2uZ9opaQ1c4VvsOjGU7L5mEkhkFKgbwPSlEWYiWE/qQIHaUukO1RyZNAVVFZ8SUrCwqSBYTPf7QUU8mGKMpMDnzIO99dObgpFv7J0B3hokusUXg6dMPefbsKev1FcWk4OGDtzlZnREj+CRL3G57iTUiTCpKEfxZoznuO2ki7RR75z71s2ecVBb6gLYVx94J/FJbXLfHhyR5uDFSFaXYeHQ9KOm4Q0yE5PFmuEYjJinapuF4PNJ3HW3Tst1siClxbFvarhs9nga72cJIDmvXtrRNy6Hc0voDTXu8sa7+MY+fiuIujyCCnsKi0KLwzHzxQW4s4RXyGAq8seR0o5DxO8mrTJB5vPkiUFAUKqf1yHiudBIiQ1adKSUSc0j4LF7y3qG15f79+9LVe+nydWFQSSTeCgNkL2+AlAUJmVUw0OXIO4O2O+R0o56qHN6CHFVnbF4Cyxge00DNZOwUb6ceyYX+OqwzfO5tAy95HdUNFo8oZLuUcmxYlbHPQHAJY4vXRuKRhpqiyPmjIiidRToBg4ai4v7nPsfq3kOqakpVCTVskGT3zmX2Rk8fhJsdgKquSSSOzYEUA/NJzfnJioktCL1jWhaYJCZewl7xcqhnr4au7aiqKlNNo9DO8tufBH8Bkjg8MnR6eWkes6lbvKE0ppRQ0VJoIxi2lgDwiByao/dIZswMj+H1ki5ToLB7d+6wOj/DJxHV2ASFMQQnhAEfErt+J5xlqQM453DaZ962IzifKamGGIaCJ79fjHHktjvnuLq8JoQg+b5VRcqe5AOjxxiRvBuTn6sW+Mj1Ij57ePc+03oiTpLeY5o9lS9xqzm6dUyVJnRHVOMgGgyaOtaYqwPdQtGqDnXoWKYJ/f6AXU5piYTjgUnToNsy73wsqq5xQYzNfIyEFAjOo6zQc4MPo0eRMWaEI270HwLbKhKH/Z7j8chms2U+X/L40RusTs5pj73cW4VAUjIpSKAGSaISvU/in641y9UJd87u8M1XF2zbA5UyNJ0wbJTRgIPQ0mHYXq2pJ1M+ffGCw3qHKUsiKt9HQs3sQi/3XRA/qL5p+ejDDzkejnSHhul8Nu5kfGbGyL5D9iFFYdAoLq8uOB4OrM9PePrkI7q2Gy1Wftzjp6K4a53TwbVCqZjpV3GgviIdPNyODBzqTlEY0WrkoA+lwFg1fm1UQqmLCF/eJynsIXh0AWUhXZh4V6vRjVFZTYoa10eWiyXL5ZKu7UhAYUux6IxIgUecGF1yN2k/MeKcqMzcrQIvk4FnUte4EJiaWuCUFCFmL/EomKrSA9f9xiRoKOqyBLvtT85rHc3wucPHYoyQ06PGIqTUazztQRThc4c6/JzPco2HG0vWSjqbpyXRGShDXdeUVSldVgpEn9AWSqtYzCpmiGlVUeV4OCswkus6SJHSFswmNdENXt7itleUJi8ebwzLhuertaau65vflSzeuQWbDK+RBIPf3BgSqnHzewljKIyj73Cg9n0vh8itIjMU+uH1TCkzj7IgqawKsQQgYo1FR+F3qygsl+a4HyMZh+93O/TEOUffdRxmU+q6loxb7ymLQiLzrKWq67xDMZyfnMh+qpBFvTQMZrSmHnZRRWElei/noAbnZUqLUoCIifXLFzz5//6nnDYliz/9i1xfXKM+fM5+6kl9hz1Gzr78FeL9N/jgP/t1rh+UbCae6aZj8crz8MFb3P2ln+Ef/u6vcp9I+fyCPQVdXfDw577OGz//c3iy7zWizQidw1Lwo0yxbmI0by+wE23XsF6vubq65s033+KNN95Aa7FuKO0ckibQsV6vR61AWRVCUMhdYp1D7SdFwfnyhPXLC9hvmNoSFxXJlGKwF3s0ji6AP3ZM53N2l1ds6qnQIFEjM0sXlkiksIboghT4GPH7hv5wpN8dMDHnRjufc5TltTCDy1QQ+wdaR2p6/LEltD3JeVT8I+AtU1UVb33uDax6gdEFhalyzBWZ1BCy9zYjb3Z4zOczYWZo6LoOrTS1Em+UorAoI6nu0rUlfEicnK7oXU81Kzm/c05hBUIx+qaY+RDkpFyvSdEI/hqhmFSURcVgNxtDIAUPETrX4Zwsxgb/m4FeGaMs16q6pijFf8Q5Q1EWOXhB7AzIfFo1OMZFEUeJUhRuR48FPxSWG1rUUJhud6HD10WVMCaN2P3t4jg+xwECGlS8tw6HYXIYFlukfBgq4eZL0lEQ+XXPrZ0BqOTE98fIwjHphElBdiOASoGURLChAbffye+AIrpIMAayR702anQuHIr00J3enmxumEc3dMUfRWEcvHyG1294vfq+H4VdAzth+Bm3X+vbEvnhj9Wi4u29WJYpBdFLlxqidNDGGubTKfPpVCYnhgM5jXL0tm3xzjGpSqqqEtFS1jeM8Nytn6tTFHYVEZ2yjYBRhMFC1wqWq5LCRMHdC2tJppCkpZgoS8Gi++2e4+98j9krmL31Fk8/+CHun36H4u1T9qonPV1zJ9akWLH+7vd5cP4z/MKXvsBv/87fZ/f9lyzedpy9eZeL3/se7YsXfNXOuSLwKvbM7p2R/JfR9RyvIkbLgsx3vSQblUM+w01HN7z2w/smPj6G3W7Dy5cv+cqXv8qdO/eyPXhJ13lSlMN9f2zou0aMAZuG+XwhLDtjhYppbGbsGWbTvGxNia7rUKaidY1w/U3C4ElRieDGBVLvJAc1JgkpN6JAT86TVBSTvc5J0xoiyXnhrwdpPBk+nll0CmlaU4wotHxuTPiuJ+RDgJvL+Mc+fiqKe12VfOHtt4T3HA3T6WIUQujRQ8WP2ai3OZ6z2Yz5fI7RSt4IpZjbKlOcjOCNMdJ2Iu4oCjhZztnt98znU05XS8nAjHFUrnovxlBWQ981tK3cZLPlEqMtRVFLCHEM9F07mh+F6HDZg2ZQpmqjRxFTAulqS8V2s5ZlXFHgU+6qQXYJQzee6V43v28ax3aj5a0LmQ0xmImB3ARDcRssj4fH7YI0PIZiOKbeoLI1cRxvotsd7XC4WC0+M5FAWdaAmDT1rkcpI2KqvIzSKQu4tMK7hCkspZ3KXsPnQut6+mMLhcVWteR+AklX0mFmHrrKYSW36YhDpz2o9gZOu7p1QH3WMhYyzCKOYr/v2hqK9phxm19v59xrB8PtohNjFFV1EKl+GpaaSoyjQAp7aawYdXl5L3z26jbGQJTUpq53uK4fn8vwug/T23jIB5kOY4yYDDNUVYVVkLwT47IMy6hos/9JKd08ctiKDZIWkkCI+LbDu56UPAtdYgvFptvxYFLxp/78L/M7rz7kycU3WcwmrN66x5f+wp/l0YNznv/gB+w/esr9N9/g5//Sn+dwPuVf+JVfZvHhJ5xdH3j17rtMzs/50ufeoDCKFrH7VUrshPu2oxh89fM1OugHhvfn9msdY8tiMefOnTs8ePAIoy19HymKGu9aOudom4brzQX73Za26+g6x2SyZT5f5PcBVIbgPIlyNqFezGjbHa5zkALBGJJRYj+iFDEpydS1mqCE9ljVRV5cS4ymsZama9BKQk3qosS1Hbos0M6iCvH7ISJ7rlKhQyA4Jx75JIrCYIwW2uaxwOskoTnZnfInPX4qirv3gTYvGVyfIGoO+z0hpjHzVJz23ChwGN54a60oIfue5tigjUYpnzMLZVRtu1YyJfNGfj/bczwcUDZxZS8llzV3XkMhcM7Rti3r9ZrdvmW2WDCbTelDIAShmqUkuGDbSoFXWgKShb0Ctigoq5p79+9ji4L9/kDX9xybA/vDgboqs/BDKE/amDHEGCXFfuySAbg57EZ16oDvfYYyNmCst6lmPkVRft4qckOxcM6NcEWIURg2uZP/rEowZCFVUPlQUlLUpfsR1o7QxgK9CxgUdVFKHq1z2RTJjYteHzyFLVAxkrwDLdYI0QlkoLWiNBUo+VydqXVDUbfWioNhLnRD0eszrs2t6+X273J7CnF9P74e3vvx9bgd8zZOAzAW+NuH5wAbDFPVsJxHZXw8L76TMpkokEghO3FaIwrYmChsFl4pjVHyXiYv0EnIh0BwwgtXRnYyCul6h+fYA0U2vErkVDGlJJdWybJv0D/EEDIFWSiYQUtnqKzluek4nZTMTiuKd+5RBcM//MG3+H7zCl0FwuMT9g8X9O98jWe/8wH/7P/995hNZ3zxL/15Xv7cPVyhefv+z3FsW37rd75JVZZ840/9Kc4eP2adl6SJ7Pqa3xfxVXmdJjxcp7cPWpmWApNpJbustqcsNGVZsdsduL5aczy2HI9HDocXHPZ7OuexpuAYDwLHWUsKCVtI+E6nIuVyBnVJLC3KaEI02fSPm31eoVF1gZnWlMs55WqGj/K7aFtA8LiUYCJ24kZl1XthMLOautCUyznVfMbV9TWdipydno6NWZWFiM611FVN/yIyi47zh3fYuYYYI2VZwbPnP7au/lQU97ZpeO+77/H8+RUxKQq7Zr3ZZiOxzIogZTsCWToN0/Vuc6DZH9ntDnSdEyOuzAsf+L9dH6QgKHBeMNGu92y2a9bX16KO06JIDT7IoiLJErFtWpyPHA4HWUYlCD5hCk3wCZetPWMK1AbKUqNtiTEWW1bia65kqVhPp5T1hN3WS7dkjEjclaTbKC3sEm208H3HYnK7wA+F5oZJEFMci80AKw0F7rWEpBAJhNcgFriBIEBSrcjQxoD/frYQjs8B0EqsklMu0iOkEwdXzkiIjLRMWVJ7UFCFSgqol0gyMmWzd47Ke4yVBbbzDhPsWLD73qF18Voo9+1uzns/Hkop//9Q0IcD4Hb4wwBJ3Z5gUspxcrcmouFzB+hneB2Gyeb2++Hz6yfF/aYDFfNEnU3NFClEmVASKCO7pRQiyRqi82JO5xy9kfdgdAdNEiTjbuH8IeTrV0tMpHchH9gCoYEiBlAqkfAjpRgYJ+UEkvsZAvv9jmPTcYxTkrL88W/8El/+IlwvPPt/9p/zyQ+uiEVBMoZXlWfxasNiHVit5pT3z3hea6qgefLd7/N7f+fvozZbfumXf4WH77zDPkm3LAE8AasliCIloTUXVudp4ve7Qt4+aAe7DaUUh8OB6nTCfn9gs16z3lyz3+05Nkfaw5VoOrQhaUtVlZRlgTGWRMzKWCXMC2N4/NabHGqDDomimuHRRAXWJAoT8UlzcnLKdDbnznrNF778ZRLkHN+EC178i6YVVVmSQqQyVhwxV0u6ruXzX/wiprBst1s8ipPVKh9siroSynLXHimLguUnp+w2G+4/OMPUohYvyhJ+63d/bF39qSjuJGj2La4LlGVFDJEU5cZMZBzyderr+NBa3CR9dpGMMWE1GZcKEjY7jHD5+hg+L0Wke1IBozOMoW3mnktRq4qKzjfsdjusLahNSds7khPhknN+9J02RlgYGlHeOdehjaXvHbNFzaye0rRtjuOLGCtULFMUlNkOVmXq2m0oZXCuvK2MDCEi7t+gUGOHeTstfnjc5MsKX/6zBXF4dF2HtZaiql/r0oei+VloI8QkCXyjB05Wy+YdQkqRpBIqabQyFFponQNE03XS7YOi78XYPKBwPqBbeS4hF+cQYDqb5u815OwKHNL3Pc45JpPJWKSLnEA1fN7wOgyFeJg+hqXc8PoOhX843D6LaQ+TAdwcCsC4jJbPE88cnw+64WcP7wMD5AbEvO/wQURacHMNaERz4JMYThXGSgeuhV/unVj+kg8xcYxMY/ZtQI1wYEAKvIqQ1R95Ygyj1YW4WEacd8Tes7lcs2gss3qOuez4e3/zb/CdY+LN/+ZXMR9vuG9W3K3PmFxHTrpAeLbjJE1ZmBmT1jK9Dlx97wNe/Z1fI32y59H9e/y93/om/+CjD/jl/+H/gGo2BVNiglgkx8GWS4POrpCfvfZuL5vHazrCSV4ka6159eoFT548YQj6uF4/x6YeW4jyOinFcrmkruucA6zzxkuTXI+NkQd373EIHndsKKs50di8W/IYHEEXLJcrlssVKSVOlktCgq53YtgWxS+qqAtJQ0uJypa4tqUwlh5FVZREJdByyMIdgRsjfScGWjFI0pXve2mMekfonOwA/c0y/0c9fjqKOxCcUOysLtCmJMWDFDsNqBwb5gcs9OZPCgnXOkIfbjIOo/izpOzJIt2TFERjxKkuBYXGoIYEc/RYDIhKVHJKQZQbuWka+t5RTef0LtL2Pa6X+LQRLvEOlzwWKdoxiEGVLkrmakk9ncjpn42/dO7UE8IYUVFluwLpIHQWbaUc9i0dZe7CU8xRZHq8gW8vRW93owNDJMQ0YtWf7dxBsPrj8UiZIaLbxW64kW7DMzpJ0tLQYQ2sh5QGWMeDUWgK2UVwG9NWGcqR5XeMkd4Fce/UhiQcVRGb9Y4QEsYUsiRXQk1MKUkWZ37uw8fIP2dYCg+P26/P8Dk+K3Rv9w23i//IDIq/P+pteG1uH6xADr6QZtnHW89h2ANpwXZv/wyFxLWBhKV4J7mZIQglOIQ4juwGSFoL0SAfcHqcnqRIm5Bl7NqilBzEkMbiobVgwimHd6sEOhewCBJraS2Pv/wVVvMHHIyGYsJu6vln73+fE225+8Uv4M6WmC4xD4pnWnN45x6TL72BWiwoj7D5+BXXmwOT0xM+bg+sZ1NmkymhrEnKoqKmUGIqFlIkKNEKDNPWMDm9NjGm26Zicu3du3ePZ5+84Ac/eJ9Pnz8nEakqy2a7IdGRUqQwtQTalDXn5+fi+58FMzHI+1NGxUfvvs/zDz5GH7donzj6I8kWMtGGFu+ORFvS7A60qwPPXzzHonE+4GNCZREhSCRlYQ0mKeqyojsc+eB777Hb7cCJqZjEI97Ao6SUqbnCvzFa8+mnzzgeDuzunvLxxx+KVYX+Ed3urcdPRXFPicxXVdnXOCBQRL4pkphbyXiZPzTgW8oSM+d5GD9jlC56pBKq7IeuNUYbghtsSTUxO0wmsX0bE5yUCrnL9Wij6bqOi4sL7j+aUBQlu8Nh9Fix2Q+DJDafyUjnHgJs99dcXF+z2e15MyQWqyUnqxXrq0tAnvPgn5OMpqxrEnYM5U3p5qKOMYyFPsVE4GYxaj7TpX4WKkj5kFC3ytjtQjcslZumISSymOR1LPn2MkseQl0N2ZtF21usEfJBFDVRg4tJ2D0p5Xxbi+t7FL2EZkQJIomjMEkosUMQNgyWw6LIHSartm1H3HsoCEMXPRwot4v57WXy+HvFiNE3S9YQAl3XjYfi8Lm3D8rhtR4K+/DfmDtrjIWQw2IyPGMV6LzkD0lYFCkIi0rnZiQl6by98/hOlqTk/FXnHCGIiK0sy/H9Hd5vpfRoOpWUxoWEzd1gjDe/mwTLJEoBJDJDI0/KSmO1ZjKf88Wf+Rqrn/si0Wk2sxl/9me/ymJScs2esy7Sobg4WVJtIjEoHv33/wXUX/plXKn5tK6JHbzxS9/gK9/4WWw64v0R4gw1m6CXC5rOkVrPfDrDWpnIAk4yem8tUG/nIA+v8e1OPiXyIS+v38MHDzg2O16+esYnn3yILTTL5Tmz6YyYYDGfs1qJueBgzicLXMPxas3f/U/+Uz7+1neYhB5cIGIJxua9XA+xw9mSzcU125NTPn3+HJu0dNhe/IRiFKjHloYYJKh8WtX4rmfz6oLj4cj6xQXVRJLdDk0rO4CswQi+zxnIRmIvjx2x6XHbA2nfClWaPwLFHZAbQoO2Cud7Ej4D61oKeVK4WxJ5sSQFWwtPFPEWE5y3j6QoWZo3F4Sc9sFLSlMKAYNABCabScUofigpwyAuydJQmQnedXz85Bn3Hj0mmQS6J6aWlHqUilidCEnCmJveU+mCrs/mTHkhHFzHYXNNs99A6LGmxNiCsqhQQ7Aume6nRH0IKh8UchGSWQSyTxB5ugRta0gGUqA0WmAppfC5aLpelL6TukINQdLKZB/skqY55oMDmq4TcVFZUlojikhb4JxHlRUxiQGVNvIxFJSloe9j5lFbQh+yfmCwgpVJQ/4uSxOjNcfmQFHIYVoag3PyPqjgBUZQnqScGC0lUVeqaIVKaMSfJGWjNGEoGelYQ15SKrGfkJt42FU4tJbnSZ7bXBC6IkkWnqYoRUHaOaaTCehIRLDsRCGJ90F8gwga5RDnxqiJTvxKokv4PuS9kRwy4tMinjXERFlW9JlP73pZAGvjRq1BjCmbJ2qCVzg3QD2Cr0totUyJ4sXUZ6iyobATuvaQf1fBtpOglaL4dVG85/Mv7kOUe7AscMFzjIlgS7wJuPaIqiZU0wLtJqyTYMqp7Wi1dKumKIQdhAIvQTRpXuBMReNKlFphVSEGW3kP5YPj0DdUlDK9Jo2JCRc1vvMsyglKW8R3ywysQYH9SKgUMuaemE4rrq5e8fzTF5Ju1fes5vdYLObMbEFR1vQxMV3eRZULtq0ccnKdglaB5tOP2D9/gpUwL7xzGBNQqScOjZYyKO8plCLlLIeIWCzYIk+1Ssn10UhTOakqUvAYFSD0EB1EL8HtIRH6XujYpkLrhC5F0ewyfTYS8Uk8slzKTdIfBRHT2AXFKBtpnWGXjLPI2J7I1pDkqV7+PePrkYyzEzF64NT8KNGDHAApRbF0VaDyBTd88yHuLA288yT0tfV6zXa3pawLGZlUGv8M0I/zknAUMiR0slpxfucO0/mCyXTCbDajNIGri5d477DBUsxKyrpGKZPthpUUEUVelIVc3Bk7tyEIG8TrQiLZbL7BFcrKcxEfeUTAYhXeB7S+UbfK6xEz9i2fbwqBUTRQ2loMnRAIJKLQpqJ3AR8i88WCvu9RSjGbTjgej3ITonC9p6rMiPMmdUPn03kZNp1KcHGRTc+IkdZ3eJ/QTqAE8e3JUFRKIjzTNr/XauTKpxSzQnCAm9KIt8hrKrYSIYAxry9CYZhKyIeEpnce3XZiHqUtSnlZWublZcqYtsrXp/dDCpcaF5zGWHHUzN83xYSPLnvtC74+QmupHxfKSfC3EQ8fDt7BpXPQGHh/w5BRGoQhm3L35+k6R1VVlEU9Tm46C4bkdpJp2DlZQkcrbKcQxMTLh4gPQud02nE8Nq+xicZppqxRKLGnTQnl5b1TSuCmgEIrw7ZtRnHVyLqK0gjYmIkQxkh4S9eJ9cN0mhW8g2hPnk+ICZMGu2qx2r28vGAymeS0KJluyqKgADCGRTVhsTyhaVoObctkMkE7BUlUyu9959v4rpVuO+TshWwFoFKeLpNcu3JS3nTQMcqOz7vBcVMJ3KIDpQai2DGThERhhv2PDxlyE8aQNtkN1hY4lwkQivwxIVzI6xZ/Yl39KSnuZJVmLsIjoR0Gc3w5pQdYJg1aH7QWNoBWnZTypIEwLl+H/w6vg840xeEiMsaIrPj2Vj4ximQEdxf/881uy/XVJXfu3cmgDqQM/kclb3EMcaT8zWYzPv+FL1GWJa8urzg2DVVVcXpyQl3X+SaRG6WqpxJcnd0aY0ykIEvOgQnzWdy7LEu5KLQhoSiKUjgQGT4prKWsZxy7HltW4jsd4zjSp5So65qmaUgpsVqtZOQtCqqBjhgDTSNe2GdnpxybHmUKYuqYTKYU1rLb7VBKcXZ2RlEU7HY7mqbh5OSE+XzO5eUVxlqqymCNYrmYUdcTLi5e5XE7YotCwi1ynuxmvUYXiqoSv3FjC6pqOu4CYs6BVUYEOVJIXuexhxDGw3JYzMk18Tp+m6+UW3+kiE4mU2KMdF0/LrE/a/8wHA5CW8v0SFNkmNAAPhdlCS+X55F3PDl8Rv6k8VC8rVGQA8mNHy/yhDe8x31/A9UU1sq0g6UsJjgn9MCUDIocUZlkP2HygT4wTtq2RSk9Xpcy6TKK8kxmkPnBs8eHsfGIKeHjcPjYW8voQRQozZbKe68QWqqqwvtA33copcWqwst9O53OsPm6ijFSVdVrbK3hPpVEMvl9yrLkjTfewDnPYS8EiL6XDIcQAno6YTKpWZ6eYYuC7W6PNobjdkNpNFYrvvlbv8Gzd98TMZFz6BhRKY6QlpSGPL1lz6vos69+3odJkInPvk+ificGvHNYpeRrXICYFfjI9DkeJmRIubD4BD7bIhDTKKIbdj2vky5+/+MPLO5KqTeBfx+4n8vtX0sp/Z+VUv974H8FvMqf+ldSSn87f83/DvhXkMnmf5NS+v/85B8yFOHMsMjYY0xkX5VcsJTOLAzJMB3sAmJIGXdPjFvVz8BRw0SQ8qmgdHZevMVxH0JAiIqkZCTOayggjfa/gpMrglY3NLOY8FmyT37TTk9P0VrTti0xRabTKYvFgroUjDBGOdS6rqese4FotBQBObDMeLMMy5bby9KqylRCCmKUSUMKgqfrOk5Oz1gsT1HbPdP5nFcvXqCA+XxO27Y0TUNd13Rdx6tXUmi7rmN7fcVsPmc5n1IYcassjGU6maC1pe0DVVUym064vr4ixsD5+TnL5YKXL2UimUwqisJQ1yWFFSfM0tqsS6jZ73cA1HXFer0Zb5zCStHbH/agElVdYYqCk5OpMB1EEoW4Mcp0FrO1rjEGowwhKfHPz66FN77r0t2/duExLOdu0TyHa8zYLPePGKtyF17IjmXgz2s9/uzjscH1DgpFXZdUmXWklBT3gYs/+pgnRQgqi69q6no67j0GTNnagDG9/N63WFS3iQJagy1KrC1wrkcZC1iC9wSv6JJAgHIwSSA9BKrqRtvhvEyiOnuJS5ER+2rSQPsNDGlnrvf0txbzScU8FZqx+Mco8NTwOTEmuq7PSmY5JJ0LWKto225kP1l7o1no+57JZEJKafzvcPjFGOmco6pEuFaWFaC4vr7meDwSo0y6TdMQSExXK6rJhLZtZWEZI5VWLKuK3/3mb/KtX/91zkIQ1lCO+iP43BlmkgaSC6sTUtBDyIeBpF155yBCZQuZkpR079H3RCXUV7F5QHCpfEVnYGh0jyRPZT4L83RKEvTB6/u0n/T4w3TuHvg3Ukq/pZRaAL+plPo7+d/+Tyml/8NrRVSprwP/Y+BngEfA31VKfTml9GN5OyqPHEMXIQuTAZ652YgD40I1/yyhTSahn91Q9USoMdy8kAs7KbNPgox2BsjGYWTqmc9+GzdFnTGHVSvxMY85EUXn5zUMFLdphZNJjdaa3W4LKKyxzGYzye0sNbPpAmutBOL2nhSlK5IuqRAlrC0oFgXT6ZSmabi+vh677UGoVFUVCpvNjxzzWQ1R8erVK05Pz9DGiNVCjByOR0xeOKaUWK/XEu82n/PRRx9xeXnJcrnk05cv2KzXNCcL7p2f4/oW7yakIJ44Xb9nMZ8Ro1g0xOCZTSdM6irH/UnqjtGa2WyGNZesr67QRnN+fkp7LGibPQ/uP2S723M4HLCm5PT0hBAC210YR9Cqqnj4+DFvvPEWXefp+4C1svyNoaPZ7zG56BljsOUEnxJFKV4h/jNqUvhsx04OMR9G6wEGiVgr8FvTNJLaZAxGiZx9sGwwRrx0BGowNE1DWVZU1YSQHTS7rmW7FTuFqq7yNSo/p2uFATOdindMCIHZbDZ26cPkENONUnN47kNI8rB0lD9lhr0MMSqKopIEJFMSQpRowabFFrJEH77fsCQepriqqsSTJpHZMwFMoKgFEmj7nOlaFNiizIXaja/j0F0OhV2i80SANTQ88rpa6rrmeDxyPB4pioK6rsdu/eLighACjx8/HuGgoaiFECjNTbED6YD3+z1KaSaTCcvlkqurK/auow/iUeRcR2UtldFMi4J3v/W7/N2/+R9jSDCbkkIOIB+aPaUYFOSjKFTGf4GEBusL5ECMKWstlBgBWmuJTqafwtT5+YrdgFLZNjpDlWUha26FoAUxCLHCaIM19rVr+A96/IHFPaX0KfBp/vtOKfVd4PFP+JK/CPwHKaUO+KFS6n3gTwD/5Md/iYz/MUk+qA9tpgILW2IYEeWClRi5GIVRU9UVXdvL52cOTJJ9DkqljMmqsTtXGMq6pu37UbIvi4yMFSIj2lDcFQoTDW3fUxTi5jdg9jEEvOsh3tAwBx/myWQqF3MeRavJlLOzM5RS40WstGExmdA0nbgwalmOpSgCGoXYFdw28DocDqPnSNM03Lt3j+l8yXZ3wFrpCoP3fPrpp7zxxpu0GVIZLqAy3zwD7jl0RLdhhbt37/Due+/iugMqeBSJj7cfsVqd8OiNt1guF3R9YLNdczzsefnqJd733Dk/Y7fboElcXr6iqkoJE4+J/WaDDw7fH3F9g4+Bplmy221ZLJY8fPiYyXTKyxfP2Gyu6bqW2kzyuP0mdT3h+adPKCqZHorC4glcX19zulpIWpBcSlhtOVmdjHBRXdU5pcpQ5IDm4/FIVVVSIKqayXTO4XDgImO2RVFQlRVKazr3CpuTqXzXY0zB8digtaYsLbPZnPl8zn5/4MWLV0ynU05OzjLLQ4+Y/G63x3nPbDYdNQd1LXjyyckZVVVxPB5xztFll8DZbE7bScyftiZbGEBZldLQAPv9nqZtqGzJpK6oqprtdkPX9sxm85FJJAeGJaaaalJwcXnB2ekZdV2zP+zlPrPCpddFQe8DyQV65zPbTLD/punG+1IbCCHlUHAjehPvMcaKq6MXsU1zFPZRioq26UYDNOGJK5pjx3a7Zzadst8fBaK0IvBxznF+fj7i9AOFNuQQFp/ZQ6BYLBecn5/L8zUFi8VCfv+rS+oMs03qmtD1lNry6ukT/vb/6//J+sWnvPX4ESGICCnEgDXDJCe1XGchU0qZe69FjFZkKHU4CIyxGC2Egd45CmuwhaW0VnyoENhvyLM1+evKqvp9jYe1dmw+huvyNhX0Jz3+S2HuSqm3gW8Avw78GeBfU0r9T4HfQLr7a6Tw/9qtL3vKTz4MsEZz5+55FhcsUEaWgaDwYTBSMnKSxUBRGGL0lFXBbDZBa0Xf+xGzLHUhGCE3FLUB69RaMZnWNO2RxWI2qliVllSiWSmWAD4EeidUtOAVRRLrza5txYY1+GzsIxTFECNRSebidFgAIYuqoigoynLEIhWK5fKErpcF2ny2EA5tYsRBQxCqXLte0/c99+/fZ71e88knn/Dw4UO22+3Y8c0XUgCic8SY2O/lALDDRZ+XYEOXdLNY1uNNJLDCkevra+ppzXw2ZXt9xXPvuHt+zqefPKMqa07Ozrlz7wGX11vWV1c8e/qU7XaLiondZkNlCw5dT7M/cPnyJafLJaWxhL5jf9jRHDd03YHlyQne90ynU87O7rNYnnHYb7m6vmSTfXeqsuD09JTVcsXx2LHZ7DC2pa5rFos581lN27Y8bw88fPBACkrSlJOJOO35QFVWokJMQFJM6ikkxXW7xugMeZUqF/gZ1+sNV1driqIYD+P1estqtaLrepL3zGYzNpstbduyWCywtmCxWFIUJc45rq/XpKgpq5Lz8zOOx5br6zUhSGFPMTGbTVEq3/QZrhGoLnI8NlxcXGCt5eTklMlkwu7Y0ByPo5J46PRnsxm6aTgcjhyPLfN6yb17M5qmY7PZ8tWvfo2ua3nx8jl93xGCp21bTLnCOc+du3dRSnG9Xo8US2MMy+USpRS+kxCc3W7H7nAgkv2R8oFZVRVVVdE1zXifTeYL9vv9OHlcX10zm83GCappWjabDffu3ZOu/XDkxYsXNE1DDJHV6mT8PUVfIofxZDJhvV6PzQ4gvvO54FeVhKrcvXuHi4tL1usNXdexXK14+PARq8VKbD5iYjqZsn35gv/HX/+/8fzjDzHBUeIpqymmENuBqBOExBCKLKwVwd5NNiX0w7Izp2kprbGmIIVE63psURG11CSlNSF6sa62RlTqVggPtiolrMN5ylJsmRPZsz9G0UWoNGYY33Y6/bF19Sf+662HUmoO/A3gX08pbZVS/y7wbyEH278F/B+B/+V/ie/3l4G/DFAXhubY0DQ9y4XgcM7fUBiFi36DxaNkpLVFIAQn8Xd5SaSUonOJohQBkA+JwmpRAMZEUZYyUSFjbSKJ4i8lXM5YtYVYA9R5hApO4bxjfzzg+o6+6wR9iyIiST6Iv4cVjHyxXEq3loReFqOMw9vtVjD3wuS/LzNmKra5bdtRT0rKfBC89+73WCwWrNdrQgh89NFHIyTT9z2LxYKqqlgslrSd4/riAtf3XF9fAQJ1zWYztofjiFEON+8wEldVhVKyuLy+Fi/wRZozn03ZXF5wfXXFfDKhritevnzBqxcvWJ2cURYGH/y4PN5sN/R9x3w+p2lkMmlbia4rlKU5Nuw2a5IKhNSzXM0pCsN0Nme5WNJ1jouLC54/f07bNpycrCgrgWrqSc3hKDfOIObZ73fMpiWz2YxPnnzEdDJhOp1RKs10sRwFTYLhCiPl9rSSr8F8jYlCdHVyyqPHb/Dee+9xfb1hMpmyOjlBG8uLl68wSnGyWDCfL3j06DEffvgh3/nOd/ne977Hn/7Tf5rT0zMmkwlXV2uur9cslwuW76wy/NWw30u8o/eeu3fvslisIFtGOxcoi4qy8Nm5sGe5rKirqVBzTQFJlLzb7Zbdbs+d8zsigZ/MKIqS5tjQqZbdbsvp6QkpBfaHHW3bYK3heHTsdruMUStOT884OTnlxfMXnJ2ey64hJnrXc30lTcVxd6TtWrq2lYmxEXZOWZZysJmSLvVsNltms9nYNV9eXtH3Pefn55RlxdnZOVVV8fTpU6bTKaenMqmIfuSSup6MXP3tdsdkWuWD7jhO1W3bYozhcDjkrn9JT6AYJvCMQz9//gJrLWdnZ6zXa/a7HQ/ffIu6rHKYjaI0mt/4tV/jyQ8/wBKZVhZ8D0YTVCIZTUyZxmqHgEbBxZURbqsujGQuaMlz1snmRklyU32KJK1o+45UGJLKehgrrCmXIjoGfISirkg9+JQIpBx1KfoHZeRnmVgIMy5fu/9cMlSVUkUu7H89pfQfAaSUXtz69/8L8J/k//0EePPWl7+RP/baI6X014C/BnDvbJkeP36ELQveeuttXrx8hVIvAEnKSQmMtpSljKHaKELsmc2mLBcL9oejbOa1SM6H6LiBajWwSkC8U+7du4ctC+7cP6MsixyBZuSAyOwZueFFRu9boWVt91v2zYHtdsNyOSfmLbhKkdJaVCH4eFVVQtOCzHuV5e3hcBD8b3NNDIE//scfih1oSHnJKb7e1loOhyOHXJSdc1xdyc3yla98hevra+BGcq0UzGdz9psNm/0m++DI2F9WJdPJlD6rT+ez2YgXD/BM3/eytMnQzPr6GmNkCdiGwG63pSwKrq+vePnyBY8/9xanZ3d463Nvsrm+pCwNm/U17777Xd5883NZkJJwrmWzvuZ0tqIwBu96Io7jAdq2ARJ1XZGUomkaNusN2+1mnDCqquLk5EQKcjbU6l0gRcnBJXrKSoK+r6+vKYqSeqpzl2gJlegD2rbLv58U0aIoCSGxXm9kKV1NODYt9bHh/M5d7m93bHff53qzldDkt9/hww8/pD0e2W53HI8Ns9mcBw8ecnFxyTe/+U2cC/yFv/AXuHfvAc+fv+J4aFkul0wns9EDPoTAZtOMGHddT7FGDqvm2HA8yoG7XCwltxd5XXymvRVVyWw+5zB08DlD2FiLj4H19Zrj9sB+v+PR44d89WtfYbfb0rYHvvOdb/H0k6e0bcvp2Sl/7Bd+idlsxuXVFUVViu+Sc3Su53A4CNTTNPS5mBtjmU4nzBYLgVCjqLSnMXJxdcmTD3/IO++8w2q14uXLl+NSc+i4U0q8+eabWGtxznP/vsBmZVnyhS98AWMM77///mjWNjQ0h8NhhKvKsmS9XnNxccHZ2ZkwR5TOy9PIfK7G63qxWFDX07HeTOspBDFqq2zB5YsX/NNf/yciFooejcFqcMHTh0DKnbNWViC/NFCwNSipL4vTE7AFodBMFwtmI6NHE3ygdo6EBNwXOlGXJd713H1wnxATdx/cQ9mSkBTeZzWyEsaNLM/12IjauqDtWk7riq45jjs3nr/8r17clbQ3/x7w3ZTSX7318YcZjwf4S8C389//FvB/V0r9VWSh+iXgn/6knyEiHcms7PtONufBAwJVpCQLDeckbg8XxEzKwGIxQ9g0gZQjkH0YFqcpn/jN+LOcs/R9x+GwZ+XnpMyF1/nm895ngzFPl2GZ2MmFfzwcafue437PdFrJUlUrdGYyFEXBbDaT7zfwuY0srZqmZbffs91u6Y4H7t29S9d1zBcLjCnEnS6R2QFTdrs909mUq6srTk5OePnyJW+99RYnJyd8+umnIxUypZQ7nRmnJ6fst+sRm9ztdgQfmM6m0HaEELi6uqIspeOdTqdorTM3XY8TQe+c2D3kLuF4PFCuTtFK8eTJE958+/PM5ks+9+abXF1ccHV5xXaz5dvf+jaH/WG88Xa7Ha53THTFpCqF42sibdNI0elaFgj/u207Nrsdfd9xcrLCWst8NuP09JTgpZgURUmMoodIMXF5dSU+QrmrGyaQ6XSKDyLSWl9vuL5+OU4pi8WCx48fU5YlFxcXVFVNDJJEtN3umM0iy+WSs9MzULLIevDgIWVR8uTjj/j4gw/41re+RVVVXF1d8e6779L3PU+ePOHJkyc8evSIqqrY765EeZsS1gh90Tk/MmHqes3J6pSqnI2MjvV6zdnZWe52S7lWuk68Sm4tEwfIb5jilFK5i7Z0kzld13I47LHWcHV1xa/+6n/O737rd2jbBq0VV9eXLE/u8o1vfGNkYA1L4sENtes6UhJLAlEFG2xRUU+kYK7Xa47NgUePH6O3O87Pzzk9PeX58+c8efJk/D2vr6959eoVx+OR6VSu56urK87PzzkcDiwWC1arFVdXVzRNMxat2Vx2TScnJ1RVhXOO999//7VdSd93xP4mbWwymdH3PbPZLFtrF9y5c0emVhdIxOw26vnVf/yPUSlx7/ycqYlMLbz11htsdc32uKMsDTp6bJ7QU8h6EiXOrV3foawhaYUpLEVV0vY9KolHuw9eXEkTYgjWHSmSpE3tjweMKbLwyxKcGM3tj4c8ZYow0yYzTpetd3Te0Ts9TjO3U8B+1OMP07n/GeBfBr6llPrt/LG/AvxPlFK/INWWD4H/tRTq9HtKqf8Q+A7CtPlXfxJTBiDEyLrZ4pWnjx0uNsQc7hCyKEcb6YwH3Nyagr4PHI8d11eCrWktP6YkuyEqBTlIOuT81agsPnX0oaX3HZghjCKAybFpClE41gI9xIlBdw42YHziuN3CyXIMz00poa1leXoHXdZghXMs3Y0sx9quG3HvYEqCT2w2e4wpWSxKrDJMypJSWzaXVzz76CM222vu3Dlnt76m0Jr7d+/heyehIbakqqbYoqI9dpS2ZjqpWc6mfJoCXXfgsNtw2G2pZgsm5QStFMfjnvV6jTLisdGFSOdFkCTdvseHfjQ3A4EcYxA74+urlzz9+Ps8fHBKNVnx+S99mZeXl1xv1jRtw3vvv8cX3vk8KGiaPcF1bApRAk9KTe9EsxDbHnc4ou5GUupp+h1dt6YsApOJRevIYj6jKqtR+bqYTolhJz4gWvDOpm0wpSGqROM66umUyXRODLJDCQn6EOi6VoKIN4HJfIatSoq6AqM5tAdiu6dzE1LqCSGyWEjRbTuxkd7udlxeXNE3nt36BcfmyGazZrve0DUNs2nNk49/yBe/8DbnZyuurq7RxoIuwEwoqhUhvKRveyaFptu9YndVszy5I4k9GprQ04Qeayz1bEqR3QT7vhcdRkxYpSjPzqXIa81+sxlZKcSIsgrfBnbXV/zgww948uRjPv74Q1TsqbVHRUc6dLz46H1enK9454tfxAePC/L1FkWBolJKIAWjCEQKA0ZFUnTUdU0MPcF3VKVlMZ8yq+ccj54nHz+jb1tUCDjfi0AnhtF2u2k8VTWnKmccjy/Z74+cnp5JA1EWvHz1Qu5lk3jjjTf40pe+RNMcePbsE5rmwHw+Z76oUTrw6tVzdFIZnpyK/5AthKGShWdFKQEnXdcyn0yJXcO73/4W3//d38JERyTQ6wJT13D+gGmCydWVYOco8enJjY8EBklecN877r/5Bklris2ah2+/I7kMZDPDnMLWxyALdK1ZTGe4rsOVE8q64u3Pfx6VnWGvLy85aTtms5s9oNaa0lgIgYuXLwm9Z14VKFWN0Bjf/s5/9eKeUvov+H2scQD+9k/4mn8b+Lf/oO89PIwxTGYT0DBbzKjWJWVViLw6JIp8Sg0CnwE+SCmxWp7geo9Se+lmi4Iin3ghBnzMI44Sbns9qaknFdNZzWI5p6qrmyeSBSohRejFcSL2ERcSQQk9UitxaCMKtzohEWrVZMLde/cpKgmt6LpOxkXhaGKMGa1JLSL779oO72QkN3PDcrHk1atXfO9736PrOu7cPcM7R/Cehw8fM59OOTZtpsvNqepaFs2FZbfdUhaKtj2yXl/LbqDvJAW+nBCDQmtR0bVty6FpmBlLBHrn8CEw+NyAxBAOW/lBRaeVonc9lxcv2awvWSqxT3jr7be5uHjFJ0+fstvteP8HP+DBg7sUhaU5Hrm+TlTWEILLthAG33uO+4NkzNaOpDyoQF2L77tWUNWViDt8FDdEbuhu3nWjMVhZlcSYaFoJZAlerIOV1szmc7FWzr4szjl2OW8zxEjX9/gols3eOY6Hg8CARpaBn376Kd4Lhry+XgtJTRvKqmY+X6K0Zrff4kPkBx98wBe++EXe/NybPH9xwfMXL3jz7S8wW6ywRYk2lq7t6EzAl4Hd5prNZk1RFaKmJGuqszzflgU6QGELKiYjw+S2X9Cg/ZDFv2d3OLDd7dkfDux2O45tizaWuqwISFB7jIHN9QWXr15wenrK4uSMcCtlqq4qbP6eqe1w7kb8pxW0zZEUAyerJbPphOA966uG9fpKCAd9T4pOrt0YRb+Rdz0xwJ3zu0ynM0iKw+FI0zQURUnbtmw3O0J0vHzxgulkwn63wxaG89NT4skCW1hmsynPnj3j4uKCxXSFUprZbMF2t5MF/fkZSmn67oZ6qZTBKLi6vOB3vvkbVNYwPz/FGE1ZT6jnUx69/QWaw471Zk1XT6S4QxbMacHOETbecrbg5OwOLkW6FFmcnIjxW2bA9G2HAoJW2EKswm1R0DrHsevpYuRysyEqIX1sNxt5fxETwcIWNMcjpbGUSrNZ70g+kOqK7U5gKGu7n1hXfyoUqpBG3KzvZDM+YMnayEKsyJmRAyVwGCcHeGI00LIWFbKsmiETNaCtKE3HLgeyH0lexOTO5/YGevhbzM5+xmiC1lmgIeOxy0ZUk4nQ9qq6vnHz44Z7P0SiFUVBMH70GW/bFhCV3SeffMKLFy9IKXH//n20TlxdXYm16GqFHrbzCaaz2UhxCyERnOfy4oLvfe97HA57ikJELM512WfdvGawJbmu4rPisupteM2Hs3zg+ac0eLXLlLPZbLi8usRUKxIl8/mMhw8fcnlxQds2XF9d4VzLfDZDo0SybhRd3wMJoxTO9TRNQ9c1VGHBULjLQgQpZHl23/dZjOKRATDhQ58hCnHOM9qgM5NAIg7Fh0chQRZVFlD54AmFdF11WYz/XpdVpsrKQScxbaJ5aJqG5tjSd52wcVQUgZSFajrFlAVlXbPbbdgdGn7zm7/Nn/ilX+Ls7JSr6y1Pnzzhzp37VGXJtK6IwbPfN0xr2O22VJevWK0WFKUdxU3DYjCGhI75GjI3WbDDtT7slQYWlvMeHyTaz8dEyjxrUxToooBUCO7sHMfDjouLlyyWK2bzlXC6ERiqKIps8Bnx6rYGILLdbtlut+PCclDstl3DfrcVEoNzgMT9ueAptJa0NGu4d+8ud+/eYblccHKy4vLqFU3TsFots0jM4lvP5npNd79lOpnw3nvv8uUvf5n7D+6y2+0ojBWfpOpmZ3R9fY0xEtwzCNIGB0/vPVVhIUX+1t/8j/n0ox9SaUVhoCgsu+ORqj1yeXXNfnvFp59+Sn9sUTFKIEqSBrRz/eiBr00hNh0psD0euHN9TZOnk2qoRyHSRS87PaWZlhXb7VbgqCEBLr+PxhpRYhuBlgfVcVWUVEbqRp8PmSGkZ0gd+3GPn4riHqNgpmVZ4gZsMYjCLTthADfS5tsCkiHE+LbzX4xpVCfKhZmDoIG+7wRXzrFkQ7EfOmylhWWjlCjFBjxvUAeKBalcMAOtcPjjvCdlvLLr5FQdLrLbtqyqTBz2h3GxdjweefbsWV4KCk6YUuLVqxdYUwhjQ4ugJITAZDKlnkxEvu2kcO+2Gz74/ntcXl1mcRWjmGLwAC+yb7xQRm9oorclzenW7yzujRJ/Jl1yHAUo6/WayXyLLQTjXa1W3L9/n81mjQ+Bi4tLjocjJ8slqixRGScGSY4KwdP3LW1zZOY7FHH0IJeFXcLnTtuYQnQMWnpb7x0p+fx52XHRFsznC+mauhZjy9ff3xjFpCn/rkUhxVT0BIaUZGlrlNi17va70U1RZ0cWpTTKFtl4LovvtMKmyFyfopTik+cv+fXf+C2WWY28362FWRUkJUjrRNMe6boS2zQcdhu6tmEymxKDx/UdZbYpCDEQw2BsJ3Frop+Lr7FD5DqTVzcpsUpGG1Je/pmilCxhdBbjKAie7fqay4tXnN+5x2S+FFhOJaI2RCUmemVZjlNy0zTjbuPu3bvcv39/vAf7vsW5LjuyR/FqyY3SdDrh7p07EkKtagZV8HwxR2vN4XDk7OyU5XLJ8XjGyxcvaNuWFy9ecHYmr+vTp08pK8vTp095663P8c477/DRRx8zqaecnJxSVTXL5TJfm0Jdrcp6lOzXZcHF8+dcXl5w/95dcB2lFZM+nxLzkxPu3L3DpLZcvHqFazvwMRMBfC7EYoUtN5jm0ePH9NGz6jsePX5E7xyHvBOwxtA2DQEoSinS06rm6uKSl89fsFqtODs7ow+iJu+alrqG07NTQQNCQCWheta25LDdiShwOh1trv95YO7/f3jIiTWZTOT/Uo6cUxFyOMONhPy278brkXEDPWjA3AeleUqJohTyv8mJ50qLnHjkfGchgci6c+fETeze8H2Gm2gY85VSKGPo+57dbostboQItyl3t4s7KXByIuG8z559wtOnTzkejzx8+JCTkxNCiFxdXeJC4P6dOxnvGyxfIyenp5TVhM55+l5oc8+ePmW338n4zGDNELJBVUAr6QiEI2zzayXfb5g0hg29iQblbw6kQX4tCzbppq+vrpjMN0xnkq6klOLs7JTz83Pao+RGdl3L9XWA1QqbISylpDg513M87jnsdyxPzzBKuijfZR8SkISrmPULSpbmPvT0fUMKAqUMnizT6Yy7d+4SY+TFy+cslisZp5XO+4M0pkCJIMdI/JmpkNhELcva7PEhDUYcFashhDE+ULJ5sylFkmVjWUkHW1VTXr54xZWGk5MzppMpm+srurahbw8oIq7vMgMmcNzvOR52LE8WpFjStS1VVd8crCqN8N9AZx3gk8HozBhNjHJoJgTztbbAWJkWq3rCUQ9cf3kTDIn95prt+oSnTz/m7c9/mXoyJWmIwRC8/D6DFmJgvgCsVivu3r07Kmr7vqPrGmIKuL7LtGU5wMuqykviOxIRkgIxao7NnvVaFNcyhUFV1ZRlJYtz13F9ecV2s0GpRK8166trmsOR3/nmb/OLv/iLnCyX7A9i/rVanbDPhAWV2Szk5q6uawqt+fbv/jbzSU0MnvlkQnDSgGljBOI0YvA2NHroGy8nYy3ROXovUFNV1WJhkrUK5M+ZTCdyzWW76RA8Jt4kovW9WFwPwsHkcuuan6sIo4wsa30ge5mOqvnBxnxocH/S46ekuN94ZjRNk9V02ZPiVmEf/jtQHIeO/XZ2psq80JSy2Zi6kZsP48/QlQ6HxeADTi6iMQ2Y5hAQzdi5u5hQVo8iC+l2RLHaHBtsGW869Fuj4e1iX5YVzfHAy5dP2Gy3eOe5c+dOpjZKYd/v95yenQmbA0XvHMdjC1ozmc0RFCkRQhqNuqSTCwxOoH3fZSFWoHctKlsyqAxBDKZlEq58e+ctr1vklnNiGoIupKjs9ns26zUxFUDC9WI1e3p6yn674+LylRRnH9hpgzWawhpSEOc8mSoc+/0G5zpIHputjkMIFKV83xADZEgoBEffN/jQizGzhhjl5j05OaWua/rec1hf43xPXdXZTCjl18oj4edyKBTFiqoUa9+ud6Jujp7oPV1zFCOr7PWdMlQTxwIvBVXe08G3RmFtgUqJ4FouLl6KrW9RYBW0zZ7msB+D3pWCrj3Q7HckL9F2KUZ876jKWoJcUswJQBmm1PL3lOKta0qgxxhv7gmlJGegrCpcV1DXFbEvcLEXeNI7NtdXTOcLpvMVh/2OyWQGyGGZIN8Lg0GZqEKrquLu3busViuBGPO9GKNDqYjzPSoFYpT3TNS6JxRFQdP21LVg75vNhqurq5HdJIdIxWQyZTZf4FqBaXVUtH2L937kyF9dXfHkyRPeeecdLi7XvHjxgqIo2WxEzTqfLwWqJWs7igLXHHn14lNi6LEkgu+pq4rO3yhGrRXodwin1/nvLosNQ5RiW9UVKSm0MRx2e8rJBG0MnXcCnd4qvENzMPi0386ClcYgmwBmvUlIEZXhY9/3TKpa4DYfRgRjoHf/QY+fiuJ+e7yMuUin7CZmTTGmsYwvkr6JSjtmzu9Y7K3IkmP+eqVuEuO99+iuz9Lkm/DdoQiHJAVgCIeOP0Lm633Aa4/rnRg+6QKyTLl3Dh9vQnxvS4WHXUHXdVxtX3HY71iv18QYOTs74+R0SUxeRBeHLbP5lOl8JrF0WiyE276jLKXDiEAKkWPT0LQtMUqXe2OcIB2oLLU83stBZPONLzYFblzS3dykt35vBotVnSGOAEGBhrZtZVKwU6zNIcsZKpvNZmx3G9qjJ5E4NkeM0SwWU7GDSEgX7XoO+x3Hw07i4XROjleaKmfPDlNBjNK1i8WtfF9rS+p6wnyxlAUdw4Ht2Wyv6cqaqqjGYjekGw2HbHPcY/RclM9WMji7tmW/P3I8SgdochcclTgFxiS+3TJNJELoCcG9dp2I6MVK9mnfcdytSd4RfY8iMJtPc6IUEBzNYYfrWtJMaL0heCmqOfc03yWAysV9gGQGA600HtJaWcpC0xw9RsHJYg6uoSsKWgkAxvctzWZL03u0LZgvT3n18gWr1dk4hfbOQz7cN5uNiNsWC05OTjg7Oxs7z+G5dH2L8z0ose0myNdWVUVd1+PvEYJMkrvdBu8d0+mU5VLw58lkymK+wrsAruVwPKCAsijoe0fXHtnv9wBcXV3x5htvcOfOOVdX16OD5EALrapqXKiiFBcvX/DqxXNC32OqEq0gxCHcRawlYoK266ShyOIlY+3I09dYCq1ISlHakslsytVugyluvIZShmxTTDloPoyeOoO69DaMTK5jQG56cqKEMWC0TBJRTA3jLdO725bdP+7xU1HcBxMlWdzJLzqcdNbcpMvfDjqG160Fbi99jJLEJVS24Y0KH904XqdhcTF2UFLADMNIlvHCNLhT3vZ7yIlIyA1VVFY26tlAKKabLv+2TWlKWQi13bJbX9O17Si4ODk5oSxL9nsx0arrmtVqlX3EjdBAtXiMJ5V93PNFdDgcOTZHyVTMz1FSkETqLJatjph0jvbT2cQyZV/yMB5m0gn+aGpUXuHJhZ/5+IfDEWP31HUlN0uGdqqqYjqZ0LWNWJzGyHa/I0YxGCutou9ksjge9qyvLyiqad6RCEZZFMKW6vsuc59FA+FDT2KItdPM5zOR4OshiQkm04ncuBkCE0ptjgvMg673ga5FvOGt+Mgc9h4IdO2evsu+4xSkmLBa47VMESplqCo6XN/iekdZSCg6ShNVovfyTlSlQUdD51sKq5iVEyazCbYQDxelAvvdhuNhx+JkRfQ30FDKGLlBjXCgFAfx+1dKPO5Dvh6Nkevd1iVdYwmuRevEtLJsDfiuYXN1QXPYURm5bg/bLX3bsNts2O23LBarHACj88EsOPugPj07Oxs77dsxhH3f4n2HVpmYQKSuxGa3qiog5UzkgFJyLxSFZT6fM5lMMNpSVxPmcy9mgO5ASpGuazHaUJWKlOQ9ns9muL7n5ctXvPOFL9G2XdaDnI76DbiBTlOMrK8uOOw2GKIEbGgjOadFOQrcgvcjvJKSBPq44EezMJXvHak7bqw3RVXmdipBkrhA4SVkeDHcZPVqnZ0lh6V4vidtUYDK0xIJY24KuM43oMr7rj8MJAM/NcWdsbAMb8iA0d7u6hOMTJnhlB5fgFtFVKsbKCTkjnOIZRs8poHXutWBF19Yi4rZ+3vo4PPreLM8zSq0Wyfu8DxVthO4Wb5KVmjTNDRNI91hJzTFqqq4c+ecuq5o24amOVJVZQ7vraRrtxZbSEq7zm6IQwfQNC3H5kjfCfwSMqVPpwGOEnzWB49SZX4NRWLO/6+9d4m1bc3uu37je8w511r7cZ73+laVw7XjqjhWHBtkgSPSCJFAJkK00iBCShqW0qERJCQUCwmJJh0CSAiBBKKDACFARO4E47gXSGI7rluP63rXfZ732a+11pzze9IY31x7VyVlo7iqzq179nd1dM5ee5971jfXnOMb4z/+4/9v+7puOl7v8QAzUA+H3PK92qL/UoUoDlua7s+15VvXdTpRmtWYPMTE1XZLrZnNetDJ36yZ7cXZGevjjKATxOvNpmVSis3r1KKaMZSasVZwxtB1vh2ATWO9qJOQ9wbf9SojlzmIrF0zDFRO1RjVK485Yr3h6HigEqk1ksuMLSDeEqLqj3uvn3lJkVwrNRVKnChRZSuGJm8wN3eqnBOuFjpnMZ2j5IhtPZFrSKUwj3vG3Y7SDBsW561SC1KVxid2USgsbXZDWVwxxtZ0FjabFSVVQkqsOkechPOLKz5877t8+N43OX/2MaYmNqseSQoJzGFiGlUa4fL8gqONNlatUa2hcRybXstDjo+PD5PMN1llS1Wlcsq13W9yyNr7YWgN8oI1pT3Dij2v12ucdY0p4vC+o+t7Tk5OAHj58gUhzO3fUYZabRLgT548ZlgfcefOHfZ7fZ9HR0cHmHaBP0opfPTB+5QUWQ099qZdZy2HZyo3eRNpVNRcSjM9UaN0KqQFYWi4fCq5DROih/UC6zSjmGX6XSt4lRfUnkjThqmLfLOauxiB3jWIppTG3NLr47zazX2/jMYPWp+I4F7rchpe49xLuZnJWLnpl3hNzztQxm6cYrWq6ZXBHBqipTQHJ66bY6WoQcESiGtVB5xS1Lk8pURqzvDqdlwbFFRJpEblM6yPVviuwxqDeG1iXb//cgjqy9RfCEExZmrLOtco93ykaxmr4tpqlq0VjD7gupdmu1Yrl9srxnaSp5gOzBhrWyVTG42zVIxTDvvhuqZESK1JewOHvqbj3cjmS8tEGva+3FgpRsZxBBTXpF5XOM47htVALYWQNNuvwByC9gSKo+REzqoTk0qh61Z0/ZqhX+nnEK+z2FLTIeuzzrLqezabNX3fNWlezTS1+ag2i8a4VkHJoadQim3Tql07gLUhlnPk+PiIs7OXjNOOWhPW9Xi/GH0o7OGdkABbDb33zJM2QVMEZ/U+3Y+pVaB67QyqTlpSaeW6Th/qRdH3GsLUGt807F+DjVnYS4dhu2us/aaJtKpYZraXW0qKnL94zte//jXe++63OXv+hBL3OCtYDIaK7xy2KlVxmkbmuYl5vfmWEgty5mqrvPGHDx9qEL5hxLF8zrHdAylFve5tz8NqwHnXTET8gaZabaJkzVxPT085Otoo1i0KYy7Syb3v2Awr8tExIXTs9juoTcMJlMlWKh9//DHGGE5OTg9/f4kLy+9jCLz//nt471j1PZ3RrHwJtCIKZhauGXNiFFPvuo5s8iEAW2M042/wWAyawcc2ker8dUhdGt/LhK3rVKNqIZDcjFkKbQVWboXv+2up6tbjSjHh+v7w/Om9/BMAy9QKuXhCtOTaEWtHFqvQR1EhMKSoJrKx5CKE1JqCxZFFmJKBpObF2dhDJVBKw+9rJsRCibCPsItgLxvd0lZEMuOsAmS5BfVS9UaeixBj4nKCXXLsQqZsM34eWd+zeONx3jctC3PImKdpe8jSw4GGFzEVjjbHnGyOsVXt3Jxd1BkdxnicG7C2Uxlgsc1lSlUja84qTBQjYZ6J00iOgRKj3vTVYnCIcTqYRVU5AWPB+JYZagZKCz7U2oaF1IcUDAW1SNNGNZSUoVRcNfhicHmGcEmSDikDWEc1Vc9CKs4auk6plDE3lkzIXOWRmHrAI9Uw7yfVvjaWblhRaiLEgrEQ5o5h6CgpUnNk8CrMtBo2DMOGrveIUd9VlU5VuzQMja4o9NYxi5pq1JAQhOO16u2EODfKYeLFk2e8884Xefr4KX0/cO/efUI/4J3X5mot3LlzQnZK61xvjqkxs7vctc9F5yxKyricqcagVoAeMZlUIlVq49cXSk5U68ilqKxuQX06RSl6Sj/NOOkaxVGNr6vRh8ZaWPUeUiLNI5cXF7x49JgXL1/w/ocf8uLJx5S4Y7PuyRFqdLhW0WUp+nkLlDBjc6RMO2ocwThS1MD04MEDTk5PmrVbaWJ4iZgjVeDl+RnnF+fUEBs8ibJ0/EBFVCq5c2y3e2KYkE6zam2kruiHHtepJLJUAVupphAxRAymXzF0PeI8437HNI1aOWVN4OwgnF9usd2AG/dUZ+m7pNLINZLDzPnZGdNuy3pYaaVXK1NU1ycpFV+VummlYEIib0eFkEolZNU/SpWDzaMt6KCXNQ23p5EYFCVQY3ftgYFtkFqrahpMqr0kqLXNagCbvsNbi8mZGqPC0u0gSjFQrCGmwhjmNu/yE5C5h5h4+uyc3V4/tMvLnToKN6S3NEaAZNVXVx/LgjHCBx8/xhjD+cWVEuhEx6aXhtN1pq/m2ts5MEbVerm81FJuKbFy05QvJdN8biilMmaFd8b9yDQqlpvqlr4z3N/tOL17wmqzAaueh0upPM3TQS9H5Vb1e4MYVsPAql8pLTE3Y2lRF5q+61XfXdTSKxfN8BQyUXpjCHP7f0+EeVIDkWbtZkRF1ATV6Sg5IR2Is4hzSE1IaYYoLM1TDt6ZuR2MmsXoIapojRprGxFMFUyJlJAJRWcGxHfkAmKg7zvSZImahjJPE+N+JAadfO0Hz2c/89MM/YC1KEyUVTMoxnCg+U2TV3lclHXSeU/Xb+j6zQEXLlmHo1QfqJlIGA1kFhUpMznjxfDsxQuMs1QDLy7Ouby6Yr/fc/7iJVdXW6ZpJAX1Sl0Pa22+i8Eah3OGoyOVBbh79z5v/+zPsek1WGxWazpnmacJL0LvHTFV9lMAZ7R35B0xqrRDrfro5UaBSxVi8yvVjNK0bF+hNsmmZfOawAy9Y97vKWHmxZPHvPvOOzx98pSHbzwkl0pnhcFbwtBB58jRQ6lYFIpJEkn7CWMUArUi5DjrZKWvbPcjb771U5zcPW0QmVrJmdbkA7i6vOTpU5XqNTdgPec83nf0w5r1ZoPzDuctIpVp0hmQpeKwdjGEF0wuGCsMKx32qtNMs2LF+YH1kU4GT/ud8upTomxHrO/prnaNPi3kvsdbw2YYSHPhG3/4Vab9iABzE+ej9eRKC9q1ZqiF3lh645TvL+XQHDUiKsPRMHDvPPOsz3SKkZQ6rcqqMmpKsyF0XfuccyHG6yngpapYIFaLSqzUogOJjfeniVFLDJ1zGFGpE3Hm2jjkB6xPRHBPKXF5mQ6GAru94q/1+zp7pjX7FhhYBC4vLnDOMk8RYyAlDsbay88V9bvGeYhBrbFSKkSTGz0QpZs1rO1g1t2aHrUNU1kKtjU7vAVnKvvtFSnchbqmlkzJOnatvYOGoS54cGt6Dd2qTdVy6B2AloFqKuEae0eFhpYy9mYjOYaZnKI24Np1U0MOwRl1QVL7r2tHHIGW/dX2C3JtrlVWGz3a7FEThg5PNZaSE6aqHAOl0UmNXrNSCjkEMgZXdbijPTFYowYhIQT22z0xtizHQU7xMOilGGzCtpJ/eQCWfsVixqyNU4OxfRsqkgYbtSysijoldU57Fhnmq5Gnjz7kK1/6Cufn51xdXuL6TrVoQmBuJul9M8GYp5kUEs46Upy4PLtgHEf6fmDc7ZjDSEXoup5htWG9OaIfNvzpz3+B09M7zYpOkFowtWj/oxTEyuHAKlWhv9revzMWjFZwoQUA79uwFFBSwjca4uAt07Rn2u05e/aU8+fP+ODb3+JbX/s6w7CiFoMBjDhWqw3ODwfWlK2qT7PdbTG+chbPKFGwzpNzBWOJJRHHzPrkmOFog++7gy47aPDurCPHxMvnzzl//lIraucUMqg6R+G7jjt37nJ0coKzntWwZtftSfvMfj82C0I1p6GAGpqoBd3Q9USfGqRm1aksJ6wR+mGtTchRNaDmWHn66IlaVXYdzjpqLgwnJ2p9l+Hp46d6r5RCiBHbGpylBQZx12y63VZ7Yoc+X61st9vvgcNqrVTf8+zZS6Yxcn6+xU+BWHSyzUizyquw2180me+OEjRxvbjY4rszQqrEohBeaZVSQQkc4zzhraP3HXGaefr8JVItkiOXV5fKpvlj1iciuC+Bt97IIpfZJFH6OdcTozS2QCWlyjAoVXJRsXOOxoRp/OZmcGvt9QDUauhaoBGOjoaGx6uWByIHemBtF3qhxO13ws6MWGM5Odk07Dix312yXjlcbyhNkdIKOGOpDfoQKt4ajHccb45wzjFOc3Njsao9YsyhN7D0Dkr93sZvLQUqjNNInGelOmY9DJXqqCwjDrij/nxuPYRcy4Edo+ClHhjOe4b1qvH3DSVL+7cqORhKThRxSFED8MWFvVAOwbymjO+cCmb5jmwdF9NMnAOd83RNEKnUdMD3U5NZTkmx/5Jzsx/TwylMM9M4stlsWDVP0lJVn18f2Ca7bMA7i+90CpdSePHsOe/87u/z3W9/hyePHxPnwPHJMauVR6zB+wHsGmsNg3WIGK6utszjhLMeayw19FAix0cDYX+JNy3jChMX456r8zMq8MF3voV1nocPH/LwwUPu3L1L33lMUew965AwzuqBV6v6B2TJDFaVBkNLCuZ5Vt9Za7VMF6G3+mu7m/jGu+/y7le/zPMnTxis4fmjR3z8wfuqs5S1kTnG+XB/UUEweOfpXGvO20ocExSFAUuFcQpcXG45feMNNifHZIFUEoPrceL0vmpEg/OXZ7x49oz91RUpqlGGE49zWkENqxUnd05ZDWs1P096AIAlZ9Xu8a5rGLlWkIckqN0b3ns2R0eUy8x047oADOtNU8X0XFxcsN1u+fCDD3nj4QM1bsFQQuL5k2d89Z2vwHiFtQbXelHXzDLVY9+HiQK8eO8D3n/vvcPk+00dK7gmeHRHd7j34AFPXj7n/GqLOEvIqZl1OFxLTlKMWr1YixfDk0ePefToEc+fn3Hn/j0yeqjUXEg5qUSHkdbrq2yGFTVl3n/vQ7bnW2LY8+zp4wPu/ketT0Rwd85xcqK6El3XY91Ve8Cl6YY0Gt+S6XI9fHRyrNQnKzRlSIO4oW2+uSRlzRgrOvhyenramqMjDx/e1ayjZFwTjF+YCjrIkdWNBeHSWyz67989OUbI5BLYXl2yWXWs7EYx0wbp1NKCctNEUfGwHjE6rKDQBwcuexWFRohRETpjtV9AUSpgM0wwxjBPM7lZ/FFLw+fMYd9GGvYfdZBIetr7yiDlejDLeFIEk8GaAcjkpHz2VBQf1uaq7kMEHb9eNA6MQRoWSSmUoA7uaQ6cv3jJ9vIKclED4MZuQuxhKKo0mCHGhJkmMA5n7KGBO9fK7mp7sJfTCkXpiYsEtDMG3zkNplYYd1sePX7EB9/+DrvLM9568z4lTlxdXHDneM2Dh/fwQ0coGePUAclbR4qR7WbFNM56/Qp4A96IjoqPW0ruGuymE6vSnNdzLnhT2J49o8wjeZ64c/eU/bgHqaw3a1xnCSmQk1NLRGMxkhrc1WYJWplfUlRT7b7DGohh4itffof/9//5B7z/wft0zpLnGXLk4vkL9hdXTNsJ6Y9BYAqB3TQSixISaql0xnK03ugUsq1MU2SeAuN+wvqZzSn0w8Bqvb5hCqGQZhvVogJXlxcKjUyjTtumSD+s6fsB5xWSOTo51R6UMVjniEklvBUydDjroUEPBq24Bu+pfU9Kmck7VsPAerVCaqXzjt3VlmmayDmxWq8Q6+h9zxtvvMFmrxr0Tx49ZuU7TvuB4/WGL/7j3+Oj776PrUq9tC2wS0vilDvqKFLphoEnH3/My5cvDxX1TQbYcv+VUhhMz1GIXF5c0a3X9KtB7ydjcFbpkznpBPluv6ekTO86ri4u2V7tGFZr1scRjKhHbtbnbo4B47Vyn6aZHDTwz3PkarsjxS1znFsl/8fE1T9pYP5hLOccp3dPsCJsNsc6XBMUdzKysEUMyMJYUWzYe8vQe+6cnpBiUHsqESr+OnMvhVJso/Aphazzqu29HxPrdc9qpewM4+yh662NECHmhC1Vp/pWHeNWIZS+04A8h8Q8TTx//ow3/KnqeID+21nxbqpi6n3X0XWOggpW1XZQSCN4i6i9XwG8aHPJ2OYi5By7GLVrbi2kjDQM1VmnJtFGGrNIIZFlQm+aJob1BmrGSMVZoYjgbEdJGSuQkhBKJs4TIUwI6uOaQiC3Dr+zyiJIJTKNkxov+16tBK0hzxPUQJgnXr58wfnLM6iFdT9QimgTjlapWX8YwrFeqY7jNFHa6PhCZ/PeN1ikPzhUWQs5Nnln7xnWOlIeJ3UN+s53vs2zZ89I48TQe6wxrPuO1Hd0znJ8tMYPHVOKuK5jNQx4a8nZU6OKTPW+10M+zJATp8cbnvWeGGqbts3Kb+8HVdJs06mlFIZVx/37p/R9x8uXT9ntduz3K46ONiAQjWr/265HyowpBUpuldiMlTUlzVjT03nDt775Db7ypS/yrW9+i6vtJUfrjhITrrOs/Yr9y3No/y6oiuEyLyLSgkdKTLmQY8J7h5HCbrvj8mKLEU/KlZ/7whd4+OA+WGG1Gki1aAZaK/M8Nvlby3Z7RZgn9cgtC0PLsVqvddKz61ivN7iuY/GPnWad8sQI640a2pTmQ6xWg44w7+mcpeZIDpNy0kvFUDjarOm7jt1ux+XFBTkV9rs90agnwhsPH3Dn5ISXL5/z8QcfsDEGuXuPd37/93EoAybGSK4qNbLAK855Mlo9L5PYC2V2uQ/7vj+4eS3DicPJfU7u3CGUzMO33mR1tGHfZjKctcQ5aHCPKpEsDW7qvWLzb7z5Jm9+5i1ibnLIQaHHQkWsMnJiiFggTjNhmlj3K8Ks+kQ3xQl/YFz94Yfqf44l2t9QaU0QCp0zraFAo4stk4FK5YMFq1aamXaqldzvvDsEWG0YLhmIHtQlZ6wx5BhJYaYO10MWUjXwFhovtRakKICmaE/LYWrrHIqQaub88pI5f8DDh29yfHKEKU0rJRlEPN67Zk7ckG1ZsgAd3JIUccY3Voz6pxojUBeqZ1Xruv2eOBedXiwamKsxmr06gzVKyVtgrFpUy6QWVUd01pAkEuaAqfq9eZo4f3nG0ydPeP7iBcdHG+7du4s3SgNQVl/Rqqppy1xtrxoMNGsm4tekKZFCUihlHqEWNQduQlbOquxAoU3yhaCldt+pTknVMvbi/BzbuPLWGMI8M+73h/FtawzWG/reE2IghYkQZj7++CM+fvSx/n1rsehnI86rOXeOesC3im5psi89GtqQiTFCqbnpyeh4eq3XGOxCD6UWvNXD1YhSTa1x+N5xcrJSZ6jOQekY+g4vqAlyqa3hFpDqVMRKKpve48iYGulcR5i3fOPr3+br775LDJEHD+9y7/7xQbXRVGGwFlcrXoTT07s8ePtntE9RCnOK7OeRaT+qQug444xVyLNmdrs9XeegZJ48fsTHH3zA5/7UT3Nv8wauZgyqH28rdEahM6miypa7LSJwfHLMbrfj/v2HnN65w+XVpeL3ci22N85Bx+dFZxNijAyrjvWqBzKQOTu74KMP3ufZs6d89OGHvDw7P0hqDMPAZz/3OY5PTumc4/Tk+NBvSbMOkk1jxTvHydGa2nlSmPjaV76EpfBT9++RRbN037yYgUOjOFP5/M//GR7+1Jv0MbMeVgeqolKTr+diFq2aO298hmG9Jht463OfxXjHlEJrhFvCNGnVXArrzVp7QaIHZQgzDx484K233jrw6WPzX40lI3ZBJjK984zbHVIqpyenvHj0HiuvOlGlZH7vve/8wLD6yQjuVJw3hBIQqfS9J85Rs1tNuLUgFOWbG9GArlzrQK2ZzjtSNNTayls9FRruvOjOcKD0GakszvSGGwybnKE1HxXjVvbMwhvXavUA/qv+B4YpZaazl0zTxBtvvMndO6eaSZuWWTurh1TOFCOYWgEVtaohKPxiTTuO0E5+iuo4ZQzOWLy3TZ+lYeoJGn9OsfrGg68YrG8MmKiBvJZrbZSaEuN2y7jfc3l+QQiB7dUVz54+JYZAXfUtMDttDlJIWZrQliGmQCkJ1+vDMU8zvenIITLvJ2qtdNYjxmGsQZZJS6uDG6kksIoBp5yb+5SKKZVqGMfxgHkC3/P7MAz0vmOeRlIMxHni8ePHfP0bX+P582ccH+sQS4pZp0VzM0+obSK3VB1EiYm89DRadrh8BlJUBqGS22esA3U3ZyJqKRhR+qpr2ZptjWzrDGIhl0hMMyVHStSmqfMqRlww2vAPgbLdIdPEIIVpe0GOI08eB7bbS168eIqzhs1GXZ3EqLy0MxanhFf2F1fkOXLv3gM+8/afYj/uVSccbczN08Q8TozbnfLcjTCHkZwjJ8cbVv2KDz/8iK995Us8f/aYP/uLv8gv/OKf4/7Dh3QYBueZUsZYd9A1sZ3n/Pwc03nub97g5M4pw3rFxdWVsjmMOWTx+/3UnsaKdXB654i+95SSGHrH48cf8v573+Hpk8eMO+15rDvh6UdPefHiTB2tUuD45FQH+pxnvdmwXm9YrRzrtacftAG9u9hz9uwZZTzlvW98k3unR9TNistpR9f3GmDhIJsrxmA6x1ufeYvj01OerFaHYa2bbkcLw2W5B0/v38N1HZHCZz7zFnNO2lAFJRFMc5saR52hug5TYdzvKSXz1ls/xf3794gNutW+TGGKQSFbgFrpfYczhmG94uTkmDKe0hnarMEfHVU/IcFdlfbyTnng1himFJAqOqGHMiGyfK9RgbIsZmrNeG+vm60oZ3aBAGpdpAv06zkUSjlC0AZoTpqVxZJJKV83Ghsck3JqVMnS+O8o5cNapHZYn5AUoQrbq23LNO9x9+7dpqtRUSPvZoDBojFhD/oTpWlFI+bgbVqNygjrdFxle7Xl+bNn5BCppZJmzXznEPHO0XeOofecnhzR1/5w+oegJWKpiRfPnnJ+fq6a67NquntnkaJYfw1RjU1o2WobmnBiW4ark3K+TSrm1FQKxeCtJVbtkyibpipOj2CNQ5xoCNUeL2L0w5nnme1+x7H13Lv/kPWwOrznnBK2BTNByCkxpcT26pLnz5/z3nvf5eXLFwf9E9VM1wZYtR2mGlIbCMra9yUmHUnPUjFRP9vapokxRumiYg/6RFUMMeswVkrLtLTBdxbfHj5nW4XS7mWcEKYARge6UoxkMRSjDJDOWDrnqcYQLy/4zrvv8vzpE7770YfY3vPWT7+FcULfOb3XGmRAFsSAs46V7yhJKz9VNuxZrwecNyQUAjmqR6qXc3lF7xxH6w2rVc847rQqjIn1sCLNI7vdyPnTJ/yD33nK1770B/zcF/4Mf+6Xf4W3f+ZtYopsjo+5mifWmxWhRLI0doi1rDcbSq6sVs1JSAy+7xFRb+JW/JIpDM6yWvd4K0hR17DNuuPBvRMubWEzOEiF/b0Tegv9as1n3nzA0dEJxnm6vsN51RU6PlGIRwd9Zl4+S5yfPWNthThvGbwBA8kohVWKHsxLRu68J1MPcier1Yp0fHxgpy09Lu/VLjO0avPo3n1SyKQ8q2tcSSyTs5WKmAIUnLcgGesEh9D3jloTYgqlJqC0P1dSCeSiypMV5dJXVJStkigkcljmWepPiuSvnna55Da63LQXahv2KFWpRcY0SuFSRX9vph1DaBlC1zjirRHYWCNL4K9JP4ScMikqZmis1eGDrD+Hac2UqowZI+YQ9BvY07JkQbzDJh1B7pvGydnZGYhOoS7TkAf9Gyyu8zjbNZuthLWeru+BJqkg6l26H/dM48SLp8948fy5UqaK7p2smCQYgjXstqqsGMOknpXHxxyfnFJzYRonnjz5iI8+fL817lSrulKpMam9mrXKu27Xcymrq5YvLPK6wzBgnG2zBlqVeOswVjFZZwzGChjIGCiavZv2/8Q6ME22GdUJV8csx53Te0olA1KIjLv9wZxhv9sB8OzZUz7+6IPDMFZKiXlW05MQ50PWlXLCiJpqh5iIKZNKIWaVMM4AMSot0Xc41wGCGKs0vRbYF/mElNUAg6oZvxhl2HBD+qLWSkmRVCNFijbTa6GkRKxQXYdYgVwpBfrVgDPCtLukX3fM4w4vPSXPiLGknJnnPd6smruUaNPf6rRmp8i0lvY5kvJMyhHXe0xjcDgnWKdMMufAeYNNegB3vWc99Aydp2+GJiFG8jTyrT/8Ku+/9zGnd+/w9s/+LF/4sz9PpjI0zPz+w4eNpGDpnR7Id+7cIcSAdY5hWCHGMKyU9qjXaIRS6HtHCjNnL56xvboghJFp2pGiVhpdFUzNmJohB5zoPEPfeY7Xa7pe/VzXm16H4+JMToEw7ZQObJVua5s/8zCsWqaXMW2SVaUMlJqqB4Q9aLcs5jrAQQF2eR5EhH5QBpBzhlozfe8I+x25ZGzzHQDluqcEpXakUpnnPcZWfKeS5qXobI9+hhExBTFNrydNiPXkGolxwtiKM4JtDLvu4JHwA2Lqnzws/xBW5eB4Iq0x5WynwSG1wNxofiLahMk5QxWMWGq5vvClFKyzimtWdfOpVUhxkd7U7rxUSBltiEYdcImtPAJR0akmBoaglD8BYwUpOvSh8rQVV4SV6yhGR/JtGwcfxx0xBnUC6nqOj46aBrahkx6LDi1IFWx15F3ToJkmtrstU5h0ND4lzl+eU5Pyr5cueVXyeetTVEDt6aR5fu6nsZXCQi2BR48eMY57ZewIzDFqs7pJB4hz1FZNKClG91gkq6BDNXROR6lrok3AehBPsZ4gmeodsRSkZrwx7XBUFyVDy4ypGLEMzuEyPH/6nJgiF+sXvPjocWPxmEPZDIu2e8F5zzzviXnmzTffbFw2NfQWMczz3GRfj+i6nhKLzh6kQMmRnGKrDl07HIBqVAaq1uv5hsbAMVKRkjDZQo46bq7xlRAqTsB0vR726P0ipiA54a2QQlRz5lxIc1LLOeeUGmssoUQGCnPJbOYjrGTCuGMeR3oZCCnr5Kbo/995T8pBB9NKxvcrMJkqiSql3RMVYwXvHTFHZWeVQhYIVGyphDmQQtI+ihhWXd+oxoZx1vtGENjvCTnzzRdnfPSHf8gbn/sMb//85xmONtR55nS94WS9Johl3a0PnqWu8xyvOhDB4VktyqFZoArj5Ut2O5VALk1jaJ6DHqYilFhVKKEqp6bvO/qhwzoh5RlTBDG9HtG14I1W7cRImmesUbtAUyEahTqc9UiLeE5EPQGAbugV1k1Bp3+tYt8lLc8ZpNqau8bQdx7XebbzqLGmjXcsFOZaFDY2Rki1tPkcVUMN44ypBt/o1aYWpVUUHZajVrwY5qb53w06m7CwBItYTZiMI/FH0yE/GcGdpqFRIcekJbzRoF3RYZlqLLVqsKkiLdC2hmbOB5y61qrY8o3Gl2qcCzXXxlKspBCVwqbFlMItZdFNuabpCQtX/hruKaUQZxX+Wtgqg/ckEpVO6Y+5Kp+5CLurHWfzOfvjvdL+guCd/tzU1CEXIa6laZNSol91DHdOsOjUXDJAvdYOP1Aul/KuVRW5VGJO5JoZ91tqiZy9eMo+RIzz5JIaDVMIWU2kAVKF3OCn2pgMWi3VNj2tMFlJFbJKIYDBWI84T2SmeEdJKoNglDdJtdq8zGgPQg9ioXNWaXopEfYTeQqMF1cKAx2mF1VPOzatjePjY2xnICeVTxBVXLetsT3PM853jFOgVqE3nt47vDWqF0+FkqmladBkFZVLTY8mteBevQ5zWQOdNfTO4A2ExnDSplxtlUNAqm+Uw9KStoSxntKgvlr03tvOEzaqJvuqGxQuMkK1hlIT3hnV0kmJMM9NabBpuIte09SGbIp1uFqY55GYZnINFJLCYSWTc4RatHL0jjoLc0l0UnHW4Z1T3rsxrIcVJWkVtFmttTqoleNuQ2+Ucy/WES8uqOOebODpBx/yMhY641i/+VkVAttsFLIrAxePHrHd7diNe0SEjz/+iM989h6bzUbv26SUzxBm9vuxDZQJ1nd0ohOb1jr6XpUltWqpxBzweMQq+aKW3KQPlPyQQiTG1J4DVcx0ThvHIqJVpqioVxWdpu68Z46BVDJimwZTY+a5NsQljdZJY42VWvG90qdDmzfJOVOMwmSaQKpYWm1DfVYMK9/jrT8QRrSO1M920ROqMeGdw4lCfkPX6WHlPaUZ7wh/NBfyExHc9WHXEjfExHpQw2OValUIISfNSBYt8nKAWa51koGmyayNjAVvV63wdryKaqakokE7RdWXOWiZNxbFEjgVHlJOqTWuTcwFSjvFre2UQSP6fisqWUDN1ALOGEJVOppzMylu2e0C1uilT80sYAno1wdMRSyEoBoT14NMtOB+3dy1dmEF6YEWQxuZbjjyNClFS/n7sc0JSJtya9l1wyJzys3QQkvLWgVFwprgWOP865BRUs485cBA0b6E0u9zLQd2Ucn6EDqv0E1tkE2KmZAhI/pZVMF3pkEz2vdItV5T2Iw2O0sSPZQqIBbf94T9yDxHjFUTiWmcMZ0wdP3hepllbqL9VyqUqLoxLINurQKUopCDa2Jwy31iRCEda7QhnrPBkJF2GN1sAIsYUp7JSWcRUkrY3iuU5TW4GiuUQ4MfarMglKqDPxUYU2hj/Xpg5JTJVSGD1IzHS9LJ5Zt6UtprKo3b3sxSaCYz7T0uND/XLS5lBoworGVVjlba3q0I/aAm8NurK7bPXmJyZffVb4C5du/SoCdst1uG1cCf/6VfYj47x7/9Jtfev/nwaxlmWzwHBisHo5Cu6xSSKgWymr0sPO8liVscqpYx/bokZyI473HDSpuZqag2f1KXKBrNuOs6xlkZLgsKkJp+kxFDyuna5LtNVBsRVsOgqpapDTo1SHO5Dk4WET8dWjso2C5idqIT5YuSJqD/D9H+UjSxGY4vJujXMst/3PpEBPdaVZHNOXfgeQMHbXQxi66x8l1KWaR8tYwMISrNcHn4jFFmS7tYtahqGxVtcEA7SW27YDddbbTRaYw0mdmk1L0KNKaBNmhzC/DLv6EHkQqEaWBPYaT3PTFE5inibdCMIhWK3Djpy7UJyYLdanBSgSVrLSFEQoitl7Bct4UqWoHc/l7BuRH1B40tqCc1L2i2XYuBsGbeLVCXyrhX/ZdhUG758uAv1YRq9agS4Ha7axOXljiNpDATm6MRGEoxhNZF0+Ezfb+D8VRrqVjGVvZOGWJVpklBVJ8mF3JpVM7lWlOJqWjZXCGXhVlk6LoVIeoQSM5FD6iq/Qnfrt9N/XEafl2qyheAa0JxDZpBm8j6+damCxQO1cw/9Xk1pUDbxNpyKbiqA2AhJXLMOl3ZGumlFkKKeFGZCR2BV+aTMUYt/vJM6TS5MQfLyGuXsDnMmFp1FqHp8oS56fzEQCmrZudXKU1BUqevI1IUi5bW+1igpq5rOjICneuphWZT2MwrFgXVEFS1M2U64zm2Grz2lzs1kq61KYOuWK96jgSez6PCfLkN1+XcqiDH0Pf4GzK43qjx+TLfoMN4Ckv5xkHPOVOb1k2t11Ie69WqNUsdpoJ3jiJC3/Uk4oH1ogc13/NZOuta9aXeuZ3vDpOz3jqsGKrUg+lN5zu8c6r7vvQGRa4d5NT0Vp+frMJ7vmkk1Qb7Jq6ThiXAG/TebAzdA9ZvEPpmIr/8/oPWJyS4q6BQQYPmPAfmqA+E0uhok57mGj9sAcc6fRgXhxR1KKpNnKnBFu3Ua0cqJRfGaVYaUpmw9kozc2eIMQCLRZae/sbXpu/iGMeZaQpN6lZYGielFLCtCjEq2zu4QQ8U0zHtA1YsRRo6W0HEXjvyAMqfv+4NuM4cGj1dpzCDBvd6gGcARHJDSCtiKr5T3n6pGes6tH5VfrPeQEsWujRkNYutLXjvdyPn5xf6HprM6/JAbDZHOKeMD0Oi5kCKM2GemcPMfoogCqvlWslVq5jSLMhMb5rOeuHDp8/x3hOxzCXhBKUwpnJo6FY4BOVSCvuQMM6QckRcd/iMjdVmWIgTISRWqwFvPWVWfZ39biTFRAhqVyjegdMqruQINWOto1R92JOoYNfcGElZVIRN4Zh2rYrSOouomFTKmc4X1aXJGZtzE2Nr06yotrxCaJViI64U1ew3XasaM95ZrFEaK6EwzwEpSvH1XpkzdcFgG+QjQC2Zadq3yV1Y5axN30YOsEbIKepQW1MvtVYzY4Ut9HN21iJWVSglK33WAMZ5nLcMfcd0danaRm0wraagOjo1YaoGbuMNJc7kOFHTTG8XyqimaWEOjPs91MrQD4dDwxpDmfekpj8UmlS26h9x8HNQZkrr19nFO9YdfBSWKe2FGp1LIpeIFYP3PYWqz5+Ra+aKXEuWiAjeW1LKeNGp4tKAeO+0F1byIiV97bK0BGRpz5gVpTLHGvX1ivbr6jKUGfXztG0yu1xrSZWUlRocwkHOG1RK/HutMf/p9YkI7rkUdvuRaQpM40wthjkESr5uqtRcMU5PtAVzFuHwsNYK06TNNKXxtYde5+y18VGVoVAaFlZRquASJGNIeN8ts0ntwxJSjU2cy6qnaRbVkIHWdV+1DE2zAg0iM7XRAq2xUNSz9KaqZWmZ7XWJ1Yaj0Oy6VGG73WOMcr9DWAZqZEGNWgBUGGmheo7j3ErWjHMTxqhksTnYw5XWxc86AWwajxvBiW22Y93BTeomJazvhzYl6ui8pabEalg1RT7Dbj9h/IBxRqcSgb4z+G7AOm0CpaCO7/Vy21x69IF1FNwNOMy5JqTm+0MvxDlHFfW8xDTjhYXKZhNlFqag8g3eRGyGba1cXl2RY8T7TvsI1uhcgTFQE0ZKuzZKe3NWTVuuzi7IU6CzDjUZUpMRESE04bNlyMU5hzUWnxynk35ecwjNA7NVgbU2KqM27YOJWCrVSNP8n7Dek6MGIQBKYdztKSUdkoFaCjVXxHtinEkpkGMkztqfSFmDWj8MVIzi0i2jLClSWmPee0fXe7pOxcBAnbCMc40lZilToObmrtQG/kKYVY00Z3IVYlWqL14rwhwKCTWKube6T3VQbGn+sonYRu6pVaVG2nXMoo3QyU7tXq+tqV5ZKM3Xwl7SjKh1X947fKcMmNgkdWkBNzZsP8cEVtU5rVd2kDHSXL8WGYBrLZmbEhnzNLZ731JyZp6mxtgrzWO3ceGlwZC1HogXy8zJNI5NwjeCU0jUNVeoUgqleSvoUJzqCmkiSYMG8+G65Rj+yLj6iQjuxhjWR0eNgubYrI9IqTkglTZpWaG2pqFIU3OTm2JRXWOBqHP7YbXTPTW7twWzDyHjBwtVRftBD4palWlwnU1DzFMLxKbh0UuNDNOobkTzPDWIpbm5oEweqh5Q4z5gJB4gloocbqClzF6wUp3CTXhMk0c17XoUlZKpSwBssICUQxEhRr7HCaiwUx2TqqU5Deq6rhBUIhgxzHMgBNXqziU12Ka2TF+zhIuLS1KM7MeRvhOohWkuvLgYudpFtvvE+qijlkjIha7vsZ3HWD3cprDXErtW1qsVqQrTNGrGVjW41/a5WaselvYGjt0PA96rO9XF1Z4lFXJWq66YFihLh1p0GFOpbln0cOq6niKAsxjvsKZSi84pGKsBovM9vfNIruQuYCqcnpyy34+kZDDWaDbeoCs1c1D4L4fcRMFq8+VVqeelDF9MzC2Q2mi77z3DZsXlxQXGW2KO9IOaZKeUmOZJG3eroU361qatXzWznaaD6YYe3Onga6tkG33US05EKr7dZ1qFaRLjeoU7tM+nz0GtIE4Ph1IyXd8RwszV5SXzPCFFfRCCyYSkJtrZFHKnbKOUCzJ4ginUzh4GEnV2RT+b0qrwrvNkm3DG0vnuhnevkg2W97fg9bUWRBSOFbTCC3No8rytj5bU+2BKCiNpfykTY1Gdn1pYH2/Y7q44Pz8jzBOxwVwLzJlSVLmSHU0fquP87JyLswtWmw2xFPbzxDQrOaL3yqcvOVOd015dKoyXV1xeXDD0PRdnFxQLY4oUAymqbHUMysgSNOg757g4PydMM9vLKy7Pz4nz3AxafgJ47ur6smPaj4yjYrb7cVJ2S4NltDGaDtjvcnJ3neCHHus9UwjMc4Sq/M9rC7kWcOXa0Yk5EAvNY1QpkCot2/A3A4f0uAGe1nTMs5b286y/RNTVRm9OFT1SPfBMqioWJO0Evrbn06bjko0siXtp0sSgFUbOenOJ0JpOlXrAXkFEsfrW79SDqerfq7WQcsGY1L7OuAULFGlDrcp+WWAZ7Qs3yKfQeg1LY7Vd8zrqw5UyM+0wSYn9fiYkAWOZQ6SQ8MNAP/TkosEphHCY8uyHgfXxkR7W1mjzt+jI+8Htx5jG3qnUopKo3hhsv6IC21G18o1UfIPUUgp048zWGUyFTiymwm63o6aMiGU/zWANtvPYzuOc4Ix+z3VdM2WAmjLjOJGnGSmVaRrJMXE9sXzd8Evt8DPZYHs1CXfOc3x81IbkFuvG2gyYtX+zNOP1cNchvipqvZdiwFgdArra7pimPSFOyt7IGYuw7gYuXrxk3O4w1rB5+EBhpZypVQ29jXUYm7V31SCW6jQjttYeKKBLZpxao0+cxfseWqZoRWWxx2ni4uJctetzIez3zJ4miKcQlHWOag1d32F6HRRKjY9/89nU5mRktVoxDEMjGDRv2Mbe0aTMHGCYUq+faevcYTAphMDV1RW73Y4UIi9evLiWXqhZPVJbpmwQ1fUXcH3P5vSYi4tzDewt+Wjn3EE+O6Z4qM4WFyxQ+jMNzkpVq6oUEzEE3EpZSCNamZXG7FN2TFMIteqOZa2ldx3Bzu2gmnX/7VCap4nddtdUZq/D0w9a8v+n6/qjXiLyDNgBz1/1e3kF6wGv577hdu+v495f133Dj2bv/0Kt9eE/6xufiOAOICK/W2v9lVf9Pn7c63XdN9zu/XXc++u6b/jx7/2PV3y/Xbfrdt2u2/UTt26D++26Xbfrdn0K1ycpuP+3r/oNvKL1uu4bbvf+Oq7Xdd/wY977JwZzv12363bdrtv1w1ufpMz9dt2u23W7btcPab3y4C4ivyYiXxORb4rI337V7+eHvUTkvxeRpyLy5Ruv3ROR3xKRb7Tf77bXRUT+y3Yt3hGRf+nVvfM/2RKRnxaR3xGRr4rIV0Tkb7XXX4e9DyLyj0Tki23v/0l7/WdE5B+2Pf4vItK11/v29Tfb999+pRv4Ey4RsSLyT0TkN9vXr8u+vysiXxKRPxCR322vvbL7/ZUGdxGxwH8F/JvALwB/TUR+4VW+px/B+h+AX/u+1/428Nu11s8Dv92+Br0On2+//ibwX/+Y3uOPYiXgP6i1/gLwq8C/1z7b12HvM/CXa62/BPwy8Gsi8qvAfwr8nVrrzwFnwK+3n/914Ky9/nfaz/0kr78FvHvj69dl3wD/Wq31l29QHl/d/X4w/H0Fv4C/APy9G1//BvAbr/I9/Yj2+Tbw5Rtffw14q/35LeBr7c//DfDX/lk/95P+C/g/gX/9dds7sAZ+H/hX0AEW114/3PvA3wP+Qvuzaz8nr/q9/3Pu93NoEPvLwG+iw5if+n23PXwXePB9r72y+/1VwzKfBT648fWH7bVP+3qz1vqo/fkx8Gb786fyerRy+18E/iGvyd4bNPEHwFPgt4BvAedVnVXge/d32Hv7/gVw/8f6hn946z8H/kM4KHff5/XYN6ggwP8lIr8nIn+zvfbK7vdPhLbM67xqrVXU0eJTuUTkCPjfgH+/1nopNxTZPs17r7Vm4JdF5A7wfwA//2rf0Y9+ici/BTyttf6eiPylV/x2XsX6i7XWj0TkDeC3ROQPb37zx32/v+rM/SPgp298/bn22qd9PRGRtwDa70/b65+q6yEiHg3s/2Ot9X9vL78We19WrfUc+B0Ujrgjizzj9+7vsPf2/VPgxY/3nf5Q1r8K/Nsi8l3gf0ahmf+CT/++Aai1ftR+f4oe6P8yr/B+f9XB/R8Dn2/d9A74d4C/+4rf049j/V3gb7Q//w0Uj15e/+utk/6rwMWNku4naomm6P8d8G6t9T+78a3XYe8PW8aOiKzQXsO7aJD/q+3Hvn/vyzX5q8Dfrw2I/UlatdbfqLV+rtb6Nvos//1a67/Lp3zfACKyEZHj5c/AvwF8mVd5v38CmhB/Bfg6ikn+R6/6/fwI9vc/AY+AiOJqv47iir8NfAP4v4F77WcFZQ99C/gS8Cuv+v3/Cfb9F1EM8h3gD9qvv/Ka7P3PA/+k7f3LwH/cXv9Z4B8B3wT+V6Bvrw/t62+27//sq97DD+Ea/CXgN1+Xfbc9frH9+soSy17l/X47oXq7btftul2fwvWqYZnbdbtu1+26XT+CdRvcb9ftul2361O4boP77bpdt+t2fQrXbXC/Xbfrdt2uT+G6De6363bdrtv1KVy3wf123a7bdbs+hes2uN+u23W7btencN0G99t1u27X7foUrv8PRP2px3HwQ7UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# preprocess_img过程\n",
    "print(np.argmax(batch_y[2]))\n",
    "\n",
    "img_path = batch_x[2]\n",
    "print(img_path)\n",
    "img2 = Image.open(img_path)\n",
    "plt.imshow(img2)\n",
    "plt.show()\n",
    "\n",
    "img2_pro = img2.resize((256, 256))\n",
    "img2_pro= img2_pro.convert('RGB')\n",
    "img2_pro = np.array(img2_pro)\n",
    "img2_pro = img2_pro[16:16 + 224, 16:16 + 224]\n",
    "\n",
    "    \n",
    "plt.imshow(img2_pro.astype(int))\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "united-sensitivity",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125\n",
      "  0.95125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125\n",
      "  0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125\n",
      "  0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125\n",
      "  0.00125 0.00125 0.00125 0.00125]\n",
      " [0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125\n",
      "  0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125\n",
      "  0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125\n",
      "  0.00125 0.00125 0.00125 0.00125 0.00125 0.95125 0.00125 0.00125 0.00125\n",
      "  0.00125 0.00125 0.00125 0.00125]\n",
      " [0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125\n",
      "  0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125\n",
      "  0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.95125 0.00125 0.00125\n",
      "  0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125\n",
      "  0.00125 0.00125 0.00125 0.00125]\n",
      " [0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125\n",
      "  0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.95125 0.00125\n",
      "  0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125\n",
      "  0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125\n",
      "  0.00125 0.00125 0.00125 0.00125]\n",
      " [0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125\n",
      "  0.00125 0.00125 0.00125 0.00125 0.00125 0.95125 0.00125 0.00125 0.00125\n",
      "  0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125\n",
      "  0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125\n",
      "  0.00125 0.00125 0.00125 0.00125]\n",
      " [0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125\n",
      "  0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.95125 0.00125\n",
      "  0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125\n",
      "  0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125\n",
      "  0.00125 0.00125 0.00125 0.00125]\n",
      " [0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.95125 0.00125\n",
      "  0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125\n",
      "  0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125\n",
      "  0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125\n",
      "  0.00125 0.00125 0.00125 0.00125]\n",
      " [0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125\n",
      "  0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125\n",
      "  0.95125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125\n",
      "  0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125\n",
      "  0.00125 0.00125 0.00125 0.00125]\n",
      " [0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125\n",
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      "  0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125 0.00125\n",
      "  0.00125 0.00125 0.00125 0.00125]]\n"
     ]
    }
   ],
   "source": [
    "# smooth labels\n",
    "batch_y = np.array(batch_y).astype(np.float32) * (1 - 0.05) + 0.05 / 40\n",
    "print(batch_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "hybrid-spiritual",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_new_last_layer(base_model, num_classes):\n",
    "    x = base_model.output\n",
    "    # 池化层，对于空域数据的全局平均池化。输出尺寸是 (batch_size, channels) 的 2D 张量\n",
    "    x = GlobalAveragePooling2D(name='avg_pool')(x)\n",
    "    # 核心网络层，Dropout 包括在训练中每次更新时， 将输入单元的按比率随机设置为 0， 这有助于防止过拟合。\n",
    "    # rate: 在 0 和 1 之间浮动。需要丢弃的输入比例。\n",
    "    x = Dropout(0.5, name='dropout1')(x)\n",
    "    # x = Dense(1024,activation='relu',kernel_regularizer= regularizers.l2(0.0001),name='fc1')(x) # 全连接层\n",
    "    # x = BatchNormalization(name='bn_fc_00')(x) # 批量标准化层\n",
    "    x = Dense(512, activation='relu', kernel_regularizer=regularizers.l2(0.0001), name='fc2')(x)\n",
    "    x = BatchNormalization(name='bn_fc_01')(x)\n",
    "    x = Dropout(0.5, name='dropout2')(x)\n",
    "    x = Dense(num_classes, activation='softmax')(x)\n",
    "    model = Model(inputs=base_model.input, outputs=x)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "distinct-steal",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型初始化设置\n",
    "def model_fn(FLAGS):\n",
    "    # 引入初始化resnet50模型\n",
    "    base_model = ResNet50(weights=\"imagenet\",\n",
    "                          include_top=False,\n",
    "                          pooling=None,\n",
    "                          input_shape=(FLAGS.input_size, FLAGS.input_size, 3),\n",
    "                          classes=FLAGS.num_classes)\n",
    "    for layer in base_model.layers:\n",
    "        layer.trainable = False\n",
    "    model = add_new_last_layer(base_model, FLAGS.num_classes)\n",
    "    model.compile(optimizer=\"adam\", loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "later-stress",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型微调\n",
    "def setup_to_finetune(FLAGS, model, layer_number=149):\n",
    "    # K.set_learning_phase(0)\n",
    "    for layer in model.layers[:layer_number]:\n",
    "        layer.trainable = False\n",
    "    # K.set_learning_phase(1)\n",
    "    for layer in model.layers[layer_number:]:\n",
    "        layer.trainable = True\n",
    "    # Adam = adam(lr=FLAGS.learning_rate,clipnorm=0.001)\n",
    "    # Adam 优化器，自动调整学习率，第一个参数是学习率，第二个参数是每次参数更新后学习率衰减值。\n",
    "    adam = Adam(lr=FLAGS.learning_rate, decay=0.0005)\n",
    "    # 配置训练模型， loss：目标函数，metrics：在训练和测试期间的模型评估标准\n",
    "    model.compile(optimizer=adam, loss='categorical_crossentropy', metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "visible-trailer",
   "metadata": {},
   "source": [
    "# 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "forty-facial",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(FLAGS):\n",
    "    \n",
    "    model = model_fn(FLAGS)\n",
    "    \n",
    "    # fit_generator返回一个 History 对象。\n",
    "    # 其 History.history 属性是连续 epoch 训练损失和评估值，以及验证集损失和评估值的记录（如果适用）。 \n",
    "    history_tl = model.fit_generator(\n",
    "        train_sequence,\n",
    "        steps_per_epoch=len(train_sequence),\n",
    "        epochs=FLAGS.max_epochs,\n",
    "        verbose=1,\n",
    "        validation_data=validation_sequence,\n",
    "        max_queue_size=10,\n",
    "        shuffle=True\n",
    "    )\n",
    "    \n",
    "    \n",
    "    # 模型微调\n",
    "    setup_to_finetune(FLAGS, model)\n",
    "    \n",
    "    history_tl = model.fit_generator(\n",
    "        train_sequence,\n",
    "        steps_per_epoch=len(train_sequence),\n",
    "        epochs=FLAGS.max_epochs * 2,\n",
    "        verbose=1,\n",
    "        callbacks=[\n",
    "            ModelCheckpoint('output_model/best.h5',\n",
    "                            monitor='val_loss', save_best_only=True, mode='min'),\n",
    "            ReduceLROnPlateau(monitor='val_loss', factor=0.1,\n",
    "                              patience=10, mode='min'),\n",
    "            EarlyStopping(monitor='val_loss', patience=10),\n",
    "        ],\n",
    "        validation_data=validation_sequence,\n",
    "        max_queue_size=10,\n",
    "        shuffle=True\n",
    "    )\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "civic-partition",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/veritasal/AI/work/garbageClassification/jupyterLab/models/resnet50.py:631: UserWarning: The output_model shape of `ResNet50(include_top=False)` has been changed since Keras 2.2.0.\n",
      "  warnings.warn('The output_model shape of `ResNet50(include_top=False)` '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "True\n",
      "<tensorflow.python.keras.layers.core.Activation object at 0x7fa894b01f28>\n",
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      "True\n",
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      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fa8945681d0>\n",
      "True\n",
      "<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x7fa89450d518>\n",
      "True\n",
      "<tensorflow.python.keras.layers.core.Activation object at 0x7fa89450dcf8>\n",
      "True\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fa894515668>\n",
      "True\n",
      "<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x7fa894539978>\n",
      "True\n",
      "<tensorflow.python.keras.layers.merge.Add object at 0x7fa8944c10b8>\n",
      "True\n",
      "<tensorflow.python.keras.layers.core.Activation object at 0x7fa8944c1b70>\n",
      "True\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fa8944e67b8>\n",
      "True\n",
      "<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x7fa8944ec320>\n",
      "True\n",
      "<tensorflow.python.keras.layers.core.Activation object at 0x7fa8944ecb00>\n",
      "True\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fa8944f3470>\n",
      "True\n",
      "<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x7fa89449b780>\n",
      "True\n",
      "<tensorflow.python.keras.layers.core.Activation object at 0x7fa89449beb8>\n",
      "True\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fa8944a18d0>\n",
      "True\n",
      "<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x7fa894448b38>\n",
      "True\n",
      "<tensorflow.python.keras.layers.merge.Add object at 0x7fa89444c2e8>\n",
      "True\n",
      "<tensorflow.python.keras.layers.core.Activation object at 0x7fa89444cdd8>\n",
      "True\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fa894472f60>\n",
      "True\n",
      "<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x7fa89447a588>\n",
      "True\n",
      "<tensorflow.python.keras.layers.core.Activation object at 0x7fa89447ad68>\n",
      "True\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fa8944026d8>\n",
      "True\n",
      "<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x7fa89442a9e8>\n",
      "True\n",
      "<tensorflow.python.keras.layers.core.Activation object at 0x7fa894431b38>\n",
      "True\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fa894431320>\n",
      "True\n",
      "<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x7fa8943d5da0>\n",
      "True\n",
      "<tensorflow.python.keras.layers.merge.Add object at 0x7fa8943dd550>\n",
      "True\n",
      "<tensorflow.python.keras.layers.core.Activation object at 0x7fa8943ddf28>\n",
      "True\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fa89438c0b8>\n",
      "True\n",
      "<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x7fa89438c7f0>\n",
      "True\n",
      "<tensorflow.python.keras.layers.core.Activation object at 0x7fa89438cf28>\n",
      "True\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fa894392940>\n",
      "True\n",
      "<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x7fa8943b6ba8>\n",
      "True\n",
      "<tensorflow.python.keras.layers.core.Activation object at 0x7fa89433fda0>\n",
      "True\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fa89433f550>\n",
      "True\n",
      "<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x7fa894369550>\n",
      "True\n",
      "<tensorflow.python.keras.layers.merge.Add object at 0x7fa894369588>\n",
      "True\n",
      "<tensorflow.python.keras.layers.core.Activation object at 0x7fa8943732e8>\n",
      "True\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fa894318320>\n",
      "True\n",
      "<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x7fa894318a58>\n",
      "True\n",
      "<tensorflow.python.keras.layers.core.Activation object at 0x7fa894320ba8>\n",
      "True\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fa894320390>\n",
      "True\n",
      "<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x7fa8942c5d68>\n",
      "True\n",
      "<tensorflow.python.keras.layers.core.Activation object at 0x7fa8942cc5c0>\n",
      "True\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fa8942cc668>\n",
      "True\n",
      "<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x7fa8942fa358>\n",
      "True\n",
      "<tensorflow.python.keras.layers.merge.Add object at 0x7fa8942fa7f0>\n",
      "True\n",
      "<tensorflow.python.keras.layers.core.Activation object at 0x7fa894282518>\n",
      "True\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fa8942a9588>\n",
      "True\n",
      "<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x7fa8942a9c18>\n",
      "True\n",
      "<tensorflow.python.keras.layers.core.Activation object at 0x7fa8942aee10>\n",
      "True\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fa8942ae5c0>\n",
      "True\n",
      "<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x7fa89425c160>\n",
      "True\n",
      "<tensorflow.python.keras.layers.core.Activation object at 0x7fa89425c940>\n",
      "True\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fa894263278>\n",
      "True\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fa894209a58>\n",
      "True\n",
      "<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x7fa8942095c0>\n",
      "True\n",
      "<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x7fa8942379e8>\n",
      "True\n",
      "<tensorflow.python.keras.layers.merge.Add object at 0x7fa894237a20>\n",
      "True\n",
      "<tensorflow.python.keras.layers.core.Activation object at 0x7fa8941be748>\n",
      "True\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fa8941e67b8>\n",
      "True\n",
      "<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x7fa8941e6d68>\n",
      "True\n",
      "<tensorflow.python.keras.layers.core.Activation object at 0x7fa8941ebf28>\n",
      "True\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fa8941eb748>\n",
      "True\n",
      "<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x7fa89419a390>\n",
      "True\n",
      "<tensorflow.python.keras.layers.core.Activation object at 0x7fa89419ab70>\n",
      "True\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fa8941a14e0>\n",
      "True\n",
      "<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x7fa89414a7f0>\n",
      "True\n",
      "<tensorflow.python.keras.layers.merge.Add object at 0x7fa89414ac88>\n",
      "True\n",
      "<tensorflow.python.keras.layers.core.Activation object at 0x7fa89414e9e8>\n",
      "True\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fa894173630>\n",
      "True\n",
      "<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x7fa89417b198>\n",
      "True\n",
      "<tensorflow.python.keras.layers.core.Activation object at 0x7fa89417b978>\n",
      "True\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fa8941032b0>\n",
      "True\n",
      "<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x7fa89412a5f8>\n",
      "True\n",
      "<tensorflow.python.keras.layers.core.Activation object at 0x7fa89412add8>\n",
      "True\n",
      "<tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fa89412e748>\n",
      "True\n",
      "<tensorflow.python.keras.layers.normalization_v2.BatchNormalization object at 0x7fa8940d4a58>\n",
      "True\n",
      "<tensorflow.python.keras.layers.merge.Add object at 0x7fa8940dd198>\n",
      "True\n",
      "<tensorflow.python.keras.layers.core.Activation object at 0x7fa8940ddc50>\n",
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_4 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1_pad (ZeroPadding2D)       (None, 230, 230, 3)  0           input_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 112, 112, 64) 9472        conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bn_conv1 (BatchNormalization)   (None, 112, 112, 64) 256         conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_147 (Activation)     (None, 112, 112, 64) 0           bn_conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool1_pad (ZeroPadding2D)       (None, 114, 114, 64) 0           activation_147[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 56, 56, 64)   0           pool1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2a (Conv2D)         (None, 56, 56, 64)   4160        max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_148 (Activation)     (None, 56, 56, 64)   0           bn2a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_148[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_149 (Activation)     (None, 56, 56, 64)   0           bn2a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_149[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch1 (Conv2D)          (None, 56, 56, 256)  16640       max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch1 (BatchNormalizatio (None, 56, 56, 256)  1024        res2a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_48 (Add)                    (None, 56, 56, 256)  0           bn2a_branch2c[0][0]              \n",
      "                                                                 bn2a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_150 (Activation)     (None, 56, 56, 256)  0           add_48[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2a (Conv2D)         (None, 56, 56, 64)   16448       activation_150[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_151 (Activation)     (None, 56, 56, 64)   0           bn2b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_151[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_152 (Activation)     (None, 56, 56, 64)   0           bn2b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_152[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_49 (Add)                    (None, 56, 56, 256)  0           bn2b_branch2c[0][0]              \n",
      "                                                                 activation_150[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_153 (Activation)     (None, 56, 56, 256)  0           add_49[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2a (Conv2D)         (None, 56, 56, 64)   16448       activation_153[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_154 (Activation)     (None, 56, 56, 64)   0           bn2c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_154[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_155 (Activation)     (None, 56, 56, 64)   0           bn2c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_155[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_50 (Add)                    (None, 56, 56, 256)  0           bn2c_branch2c[0][0]              \n",
      "                                                                 activation_153[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_156 (Activation)     (None, 56, 56, 256)  0           add_50[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2a (Conv2D)         (None, 28, 28, 128)  32896       activation_156[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_157 (Activation)     (None, 28, 28, 128)  0           bn3a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_157[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_158 (Activation)     (None, 28, 28, 128)  0           bn3a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_158[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch1 (Conv2D)          (None, 28, 28, 512)  131584      activation_156[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch1 (BatchNormalizatio (None, 28, 28, 512)  2048        res3a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_51 (Add)                    (None, 28, 28, 512)  0           bn3a_branch2c[0][0]              \n",
      "                                                                 bn3a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_159 (Activation)     (None, 28, 28, 512)  0           add_51[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_159[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_160 (Activation)     (None, 28, 28, 128)  0           bn3b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_160[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_161 (Activation)     (None, 28, 28, 128)  0           bn3b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_161[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_52 (Add)                    (None, 28, 28, 512)  0           bn3b_branch2c[0][0]              \n",
      "                                                                 activation_159[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_162 (Activation)     (None, 28, 28, 512)  0           add_52[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_162[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_163 (Activation)     (None, 28, 28, 128)  0           bn3c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_163[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_164 (Activation)     (None, 28, 28, 128)  0           bn3c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_164[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_53 (Add)                    (None, 28, 28, 512)  0           bn3c_branch2c[0][0]              \n",
      "                                                                 activation_162[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_165 (Activation)     (None, 28, 28, 512)  0           add_53[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_165[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_166 (Activation)     (None, 28, 28, 128)  0           bn3d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_166[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_167 (Activation)     (None, 28, 28, 128)  0           bn3d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_167[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_54 (Add)                    (None, 28, 28, 512)  0           bn3d_branch2c[0][0]              \n",
      "                                                                 activation_165[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_168 (Activation)     (None, 28, 28, 512)  0           add_54[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2a (Conv2D)         (None, 14, 14, 256)  131328      activation_168[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_169 (Activation)     (None, 14, 14, 256)  0           bn4a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_169[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_170 (Activation)     (None, 14, 14, 256)  0           bn4a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_170[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch1 (Conv2D)          (None, 14, 14, 1024) 525312      activation_168[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch1 (BatchNormalizatio (None, 14, 14, 1024) 4096        res4a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_55 (Add)                    (None, 14, 14, 1024) 0           bn4a_branch2c[0][0]              \n",
      "                                                                 bn4a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_171 (Activation)     (None, 14, 14, 1024) 0           add_55[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_171[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_172 (Activation)     (None, 14, 14, 256)  0           bn4b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_172[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_173 (Activation)     (None, 14, 14, 256)  0           bn4b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_173[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_56 (Add)                    (None, 14, 14, 1024) 0           bn4b_branch2c[0][0]              \n",
      "                                                                 activation_171[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_174 (Activation)     (None, 14, 14, 1024) 0           add_56[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_174[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_175 (Activation)     (None, 14, 14, 256)  0           bn4c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_175[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_176 (Activation)     (None, 14, 14, 256)  0           bn4c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_176[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_57 (Add)                    (None, 14, 14, 1024) 0           bn4c_branch2c[0][0]              \n",
      "                                                                 activation_174[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_177 (Activation)     (None, 14, 14, 1024) 0           add_57[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_177[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_178 (Activation)     (None, 14, 14, 256)  0           bn4d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_178[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_179 (Activation)     (None, 14, 14, 256)  0           bn4d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_179[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_58 (Add)                    (None, 14, 14, 1024) 0           bn4d_branch2c[0][0]              \n",
      "                                                                 activation_177[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_180 (Activation)     (None, 14, 14, 1024) 0           add_58[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_180[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4e_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_181 (Activation)     (None, 14, 14, 256)  0           bn4e_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_181[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4e_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_182 (Activation)     (None, 14, 14, 256)  0           bn4e_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_182[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4e_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_59 (Add)                    (None, 14, 14, 1024) 0           bn4e_branch2c[0][0]              \n",
      "                                                                 activation_180[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_183 (Activation)     (None, 14, 14, 1024) 0           add_59[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_183[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4f_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_184 (Activation)     (None, 14, 14, 256)  0           bn4f_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_184[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4f_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_185 (Activation)     (None, 14, 14, 256)  0           bn4f_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_185[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4f_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_60 (Add)                    (None, 14, 14, 1024) 0           bn4f_branch2c[0][0]              \n",
      "                                                                 activation_183[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_186 (Activation)     (None, 14, 14, 1024) 0           add_60[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2a (Conv2D)         (None, 7, 7, 512)    524800      activation_186[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_187 (Activation)     (None, 7, 7, 512)    0           bn5a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_187[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_188 (Activation)     (None, 7, 7, 512)    0           bn5a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_188[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch1 (Conv2D)          (None, 7, 7, 2048)   2099200     activation_186[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch1 (BatchNormalizatio (None, 7, 7, 2048)   8192        res5a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_61 (Add)                    (None, 7, 7, 2048)   0           bn5a_branch2c[0][0]              \n",
      "                                                                 bn5a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_189 (Activation)     (None, 7, 7, 2048)   0           add_61[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2a (Conv2D)         (None, 7, 7, 512)    1049088     activation_189[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_190 (Activation)     (None, 7, 7, 512)    0           bn5b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_190[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_191 (Activation)     (None, 7, 7, 512)    0           bn5b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_191[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_62 (Add)                    (None, 7, 7, 2048)   0           bn5b_branch2c[0][0]              \n",
      "                                                                 activation_189[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_192 (Activation)     (None, 7, 7, 2048)   0           add_62[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2a (Conv2D)         (None, 7, 7, 512)    1049088     activation_192[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_193 (Activation)     (None, 7, 7, 512)    0           bn5c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_193[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_194 (Activation)     (None, 7, 7, 512)    0           bn5c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_194[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_63 (Add)                    (None, 7, 7, 2048)   0           bn5c_branch2c[0][0]              \n",
      "                                                                 activation_192[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_195 (Activation)     (None, 7, 7, 2048)   0           add_63[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "avg_pool (GlobalAveragePooling2 (None, 2048)         0           activation_195[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dropout1 (Dropout)              (None, 2048)         0           avg_pool[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "fc2 (Dense)                     (None, 512)          1049088     dropout1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bn_fc_01 (BatchNormalization)   (None, 512)          2048        fc2[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "dropout2 (Dropout)              (None, 512)          0           bn_fc_01[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 40)           20520       dropout2[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 24,659,368\n",
      "Trainable params: 1,070,632\n",
      "Non-trainable params: 23,588,736\n",
      "__________________________________________________________________________________________________\n",
      "None\n",
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_4 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1_pad (ZeroPadding2D)       (None, 230, 230, 3)  0           input_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 112, 112, 64) 9472        conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bn_conv1 (BatchNormalization)   (None, 112, 112, 64) 256         conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_147 (Activation)     (None, 112, 112, 64) 0           bn_conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool1_pad (ZeroPadding2D)       (None, 114, 114, 64) 0           activation_147[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 56, 56, 64)   0           pool1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2a (Conv2D)         (None, 56, 56, 64)   4160        max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_148 (Activation)     (None, 56, 56, 64)   0           bn2a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_148[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_149 (Activation)     (None, 56, 56, 64)   0           bn2a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_149[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch1 (Conv2D)          (None, 56, 56, 256)  16640       max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch1 (BatchNormalizatio (None, 56, 56, 256)  1024        res2a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_48 (Add)                    (None, 56, 56, 256)  0           bn2a_branch2c[0][0]              \n",
      "                                                                 bn2a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_150 (Activation)     (None, 56, 56, 256)  0           add_48[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2a (Conv2D)         (None, 56, 56, 64)   16448       activation_150[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_151 (Activation)     (None, 56, 56, 64)   0           bn2b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_151[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_152 (Activation)     (None, 56, 56, 64)   0           bn2b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_152[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_49 (Add)                    (None, 56, 56, 256)  0           bn2b_branch2c[0][0]              \n",
      "                                                                 activation_150[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_153 (Activation)     (None, 56, 56, 256)  0           add_49[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2a (Conv2D)         (None, 56, 56, 64)   16448       activation_153[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_154 (Activation)     (None, 56, 56, 64)   0           bn2c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_154[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_155 (Activation)     (None, 56, 56, 64)   0           bn2c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_155[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_50 (Add)                    (None, 56, 56, 256)  0           bn2c_branch2c[0][0]              \n",
      "                                                                 activation_153[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_156 (Activation)     (None, 56, 56, 256)  0           add_50[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2a (Conv2D)         (None, 28, 28, 128)  32896       activation_156[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_157 (Activation)     (None, 28, 28, 128)  0           bn3a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_157[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_158 (Activation)     (None, 28, 28, 128)  0           bn3a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_158[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch1 (Conv2D)          (None, 28, 28, 512)  131584      activation_156[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch1 (BatchNormalizatio (None, 28, 28, 512)  2048        res3a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_51 (Add)                    (None, 28, 28, 512)  0           bn3a_branch2c[0][0]              \n",
      "                                                                 bn3a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_159 (Activation)     (None, 28, 28, 512)  0           add_51[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_159[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_160 (Activation)     (None, 28, 28, 128)  0           bn3b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_160[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_161 (Activation)     (None, 28, 28, 128)  0           bn3b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_161[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_52 (Add)                    (None, 28, 28, 512)  0           bn3b_branch2c[0][0]              \n",
      "                                                                 activation_159[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_162 (Activation)     (None, 28, 28, 512)  0           add_52[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_162[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_163 (Activation)     (None, 28, 28, 128)  0           bn3c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_163[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_164 (Activation)     (None, 28, 28, 128)  0           bn3c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_164[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_53 (Add)                    (None, 28, 28, 512)  0           bn3c_branch2c[0][0]              \n",
      "                                                                 activation_162[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_165 (Activation)     (None, 28, 28, 512)  0           add_53[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_165[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_166 (Activation)     (None, 28, 28, 128)  0           bn3d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_166[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_167 (Activation)     (None, 28, 28, 128)  0           bn3d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_167[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_54 (Add)                    (None, 28, 28, 512)  0           bn3d_branch2c[0][0]              \n",
      "                                                                 activation_165[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_168 (Activation)     (None, 28, 28, 512)  0           add_54[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2a (Conv2D)         (None, 14, 14, 256)  131328      activation_168[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_169 (Activation)     (None, 14, 14, 256)  0           bn4a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_169[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_170 (Activation)     (None, 14, 14, 256)  0           bn4a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_170[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch1 (Conv2D)          (None, 14, 14, 1024) 525312      activation_168[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch1 (BatchNormalizatio (None, 14, 14, 1024) 4096        res4a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_55 (Add)                    (None, 14, 14, 1024) 0           bn4a_branch2c[0][0]              \n",
      "                                                                 bn4a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_171 (Activation)     (None, 14, 14, 1024) 0           add_55[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_171[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_172 (Activation)     (None, 14, 14, 256)  0           bn4b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_172[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_173 (Activation)     (None, 14, 14, 256)  0           bn4b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_173[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_56 (Add)                    (None, 14, 14, 1024) 0           bn4b_branch2c[0][0]              \n",
      "                                                                 activation_171[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_174 (Activation)     (None, 14, 14, 1024) 0           add_56[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_174[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_175 (Activation)     (None, 14, 14, 256)  0           bn4c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_175[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_176 (Activation)     (None, 14, 14, 256)  0           bn4c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_176[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_57 (Add)                    (None, 14, 14, 1024) 0           bn4c_branch2c[0][0]              \n",
      "                                                                 activation_174[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_177 (Activation)     (None, 14, 14, 1024) 0           add_57[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_177[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_178 (Activation)     (None, 14, 14, 256)  0           bn4d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_178[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_179 (Activation)     (None, 14, 14, 256)  0           bn4d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_179[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_58 (Add)                    (None, 14, 14, 1024) 0           bn4d_branch2c[0][0]              \n",
      "                                                                 activation_177[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_180 (Activation)     (None, 14, 14, 1024) 0           add_58[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_180[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4e_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_181 (Activation)     (None, 14, 14, 256)  0           bn4e_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_181[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4e_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_182 (Activation)     (None, 14, 14, 256)  0           bn4e_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_182[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4e_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_59 (Add)                    (None, 14, 14, 1024) 0           bn4e_branch2c[0][0]              \n",
      "                                                                 activation_180[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_183 (Activation)     (None, 14, 14, 1024) 0           add_59[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_183[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4f_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_184 (Activation)     (None, 14, 14, 256)  0           bn4f_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_184[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4f_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_185 (Activation)     (None, 14, 14, 256)  0           bn4f_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_185[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4f_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_60 (Add)                    (None, 14, 14, 1024) 0           bn4f_branch2c[0][0]              \n",
      "                                                                 activation_183[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_186 (Activation)     (None, 14, 14, 1024) 0           add_60[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2a (Conv2D)         (None, 7, 7, 512)    524800      activation_186[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_187 (Activation)     (None, 7, 7, 512)    0           bn5a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_187[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_188 (Activation)     (None, 7, 7, 512)    0           bn5a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_188[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch1 (Conv2D)          (None, 7, 7, 2048)   2099200     activation_186[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch1 (BatchNormalizatio (None, 7, 7, 2048)   8192        res5a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_61 (Add)                    (None, 7, 7, 2048)   0           bn5a_branch2c[0][0]              \n",
      "                                                                 bn5a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_189 (Activation)     (None, 7, 7, 2048)   0           add_61[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2a (Conv2D)         (None, 7, 7, 512)    1049088     activation_189[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_190 (Activation)     (None, 7, 7, 512)    0           bn5b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_190[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_191 (Activation)     (None, 7, 7, 512)    0           bn5b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_191[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_62 (Add)                    (None, 7, 7, 2048)   0           bn5b_branch2c[0][0]              \n",
      "                                                                 activation_189[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_192 (Activation)     (None, 7, 7, 2048)   0           add_62[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2a (Conv2D)         (None, 7, 7, 512)    1049088     activation_192[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_193 (Activation)     (None, 7, 7, 512)    0           bn5c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_193[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_194 (Activation)     (None, 7, 7, 512)    0           bn5c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_194[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_63 (Add)                    (None, 7, 7, 2048)   0           bn5c_branch2c[0][0]              \n",
      "                                                                 activation_192[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_195 (Activation)     (None, 7, 7, 2048)   0           add_63[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "avg_pool (GlobalAveragePooling2 (None, 2048)         0           activation_195[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dropout1 (Dropout)              (None, 2048)         0           avg_pool[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "fc2 (Dense)                     (None, 512)          1049088     dropout1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "bn_fc_01 (BatchNormalization)   (None, 512)          2048        fc2[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "dropout2 (Dropout)              (None, 512)          0           bn_fc_01[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 40)           20520       dropout2[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 24,659,368\n",
      "Trainable params: 13,159,976\n",
      "Non-trainable params: 11,499,392\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "train_model(FLAGS)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "expensive-profession",
   "metadata": {},
   "source": [
    "# 模型预测结果分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "national-finger",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 本地路径获取图片信息\n",
    "def preprocess_img(img_path,img_size):\n",
    "    try:\n",
    "        img = Image.open(img_path)\n",
    "        # if img.format:\n",
    "        # resize_scale = img_size / max(img.size[:2])\n",
    "        # img = img.resize((int(img.size[0] * resize_scale), int(img.size[1] * resize_scale)))\n",
    "        img = img.resize((256, 256))\n",
    "        img = img.convert('RGB')\n",
    "        # img.show()\n",
    "        img = np.array(img)\n",
    "        imgs = []\n",
    "        for _ in range(10):\n",
    "            i = random.randint(0, 32)\n",
    "            j = random.randint(0, 32)\n",
    "            imgg = img[i:i + 224, j:j + 224]\n",
    "            imgg = preprocess_input(imgg)\n",
    "            imgs.append(imgg)\n",
    "        return imgs\n",
    "    except Exception as e:\n",
    "        print('发生了异常data_process：', e)\n",
    "        return 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "handled-johns",
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_model(test_img_paths, test_labels):\n",
    "    test_data, test_labels = load_test_data(FLAGS, test_img_paths, test_labels)\n",
    "    test_loss, test_acc = model.evaluate(test_data, test_labels)\n",
    "    return test_loss, test_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "worst-seeking",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_test_data(FLAGS, test_img_paths, test_labels):\n",
    "    test_data = []\n",
    "    for index, img_path in enumerate(test_img_paths):\n",
    "        # 处理图片\n",
    "        img = preprocess_img(img_path,FLAGS.input_size)\n",
    "        test_data.append(preprocess_img(img_path,FLAGS.input_size))\n",
    "    # test_data = np.array(test_data)\n",
    "    return test_data,test_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "future-optimization",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'test_model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-b575c9a2b718>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_acc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_img_paths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'test_loss is: '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_loss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'test_model' is not defined"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = test_model(test_img_paths, test_labels)\n",
    "print('test_loss is: ', test_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "polar-space",
   "metadata": {},
   "source": [
    "# 模型预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "amino-silicon",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 暴露模型初始化\n",
    "def init_artificial_neural_network():\n",
    "    model = model_fn(FLAGS)\n",
    "    model.load_weights(h5_weights_path, by_name=True)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "streaming-prescription",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 测试图片\n",
    "def prediction_result_from_img(model, imgurl):\n",
    "    # 加载分类数据\n",
    "    with open(\"./garbage_classify/garbage_classify_rule.json\", 'r') as load_f:\n",
    "        load_dict = json.load(load_f)\n",
    "    if re.match(r'^https?:/{2}\\w.+$', imgurl):\n",
    "        test_data = preprocess_img_from_Url(imgurl, FLAGS.input_size)\n",
    "    else:\n",
    "        test_data = preprocess_img(imgurl, FLAGS.input_size)\n",
    "    tta_num = 5\n",
    "    predictions = [0 * tta_num]\n",
    "    for i in range(tta_num):\n",
    "        x_test = test_data[i]\n",
    "        x_test = x_test[np.newaxis, :, :, :]\n",
    "        prediction = model.predict(x_test)[0]\n",
    "        # print(prediction)\n",
    "        predictions += prediction\n",
    "    pred_label = np.argmax(predictions, axis=0)\n",
    "    print('-------深度学习垃圾分类预测结果----------')\n",
    "    print(pred_label)\n",
    "    print(load_dict[str(pred_label)])\n",
    "    print('-------深度学习垃圾分类预测结果--------')\n",
    "    return load_dict[str(pred_label)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "hazardous-entertainment",
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "请输入图片地址: ./test.jpg\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "您输入的图片地址为：./test.jpg\n",
      "-------深度学习垃圾分类预测结果----------\n",
      "11\n",
      "厨余垃圾/菜叶菜根\n",
      "-------深度学习垃圾分类预测结果--------\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "Interrupted by user",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-29-426eb00d8479>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m         \u001b[0mimg_url\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"请输入图片地址:\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'您输入的图片地址为：'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mimg_url\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprediction_result_from_img\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_url\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/AI/anaconda3/envs/ai/lib/python3.6/site-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36mraw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m    861\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_ident\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    862\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_header\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 863\u001b[0;31m             \u001b[0mpassword\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    864\u001b[0m         )\n\u001b[1;32m    865\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/AI/anaconda3/envs/ai/lib/python3.6/site-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    902\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    903\u001b[0m                 \u001b[0;31m# re-raise KeyboardInterrupt, to truncate traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 904\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Interrupted by user\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    905\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    906\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid Message:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_info\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: Interrupted by user"
     ]
    }
   ],
   "source": [
    "# 预测模块\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
    "config = tf.compat.v1.ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "config.gpu_options.per_process_gpu_memory_fraction = 0.7\n",
    "sess = tf.compat.v1.Session(config=config)\n",
    "\n",
    "FLAGS = tf.compat.v1.app.flags.FLAGS\n",
    "h5_weights_path = './output_model/best.h5'\n",
    "\n",
    "model = init_artificial_neural_network()\n",
    "\n",
    "while True:\n",
    "    try:\n",
    "        img_url = input(\"请输入图片地址:\")\n",
    "        print('您输入的图片地址为：' + img_url)\n",
    "        res = prediction_result_from_img(model, img_url)\n",
    "    except Exception as e:\n",
    "        print('发生了异常：', e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "athletic-desperate",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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